Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 10, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Complexity Analysis of Environmental Time Series.

Holger Lange¹, Michael Hauhs²

¹Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), N-1433 Ås, Norway.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

Related Concept Videos

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

Noncompartmental Analysis: Mean Residence Time

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

Variability: Analysis

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Manipulation and Analysis

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers.

PloS one·2016

See all related articles

Researchers analyzed long-term data from forested catchments using nonlinear dynamics. This study characterizes catchment system dynamics, revealing complex patterns in water and nutrient fluxes for potential universality across variables and locations.

Area of Science:

Environmental Science
Hydrology
Ecology

Background:

Forested catchments are key terrestrial ecosystems studied across environmental science disciplines.
Time series data of water, matter fluxes, and nutrient concentrations reveal complex spatiotemporal patterns.
Nonlinear dynamics offers a framework for investigating intricate catchment processes across various timescales.

Purpose of the Study:

To analyze long-term hydrological and biogeochemical data from three headwater catchments in the Bramke valley, Germany (1991-2023).
To characterize the dynamics of these catchment systems using nonlinear methods and ordinal pattern statistics.
To investigate potential universality in system dynamics across different variables and between closely located catchments.

Main Methods:

Keywords:

Tarnopolski diagrams catchments ecosystems fisher information horizontal visibility graphs ordinal patterns permutation complexity permutation entropy time series

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Related Experiment Videos

Last Updated: May 10, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Data preprocessing including gap filling, detrending, and annual cycle removal using Singular System Analysis (SSA).

Calculation of ordinal pattern statistics: permutation entropy, permutation complexity, Fisher information, q-entropy, and α-entropy.

Analysis using Tarnopolski diagrams, comparison with stochastic processes (fractional Brownian motion, fractional Gaussian noise, β noise), Complexity from Ordinal Pattern Positioned Slopes (COPPS), and horizontal visibility graphs.

Main Results:

Characterization of catchment system dynamics through a suite of nonlinear metrics.
Comparison of observed dynamics with reference stochastic processes and deterministic chaos models.
Assessment of potential universality in dynamics across variables and between the three studied catchments.

Conclusions:

The study provides a comprehensive characterization of catchment system dynamics using advanced nonlinear analytical techniques.
Findings contribute to understanding the complex interplay of processes governing water and nutrient fluxes in forested ecosystems.
The approach allows for scrutiny of universality, offering insights into broader ecological and hydrological principles.