Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

What is Variation?01:14

What is Variation?

18.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.6K
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.8K
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
6.8K
Variation01:19

Variation

8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.0K
Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

4.2K
Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
4.2K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.8K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.8K
Fixed Action Patterns01:06

Fixed Action Patterns

17.7K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
17.7K

You might also read

Related Articles

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

Sort by
Same author

Effect of plant ash incorporation on hydrologic processes of coarse-textured soils.

Scientific reports·2026
Same author

Prediction of Soil pH in Ash-Enriched Laboratory Columns Using Portable Near-Infrared Spectroscopy: A Comparison of Analytical Strategies.

Applied spectroscopy·2025
Same author

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution.

Data in brief·2023
Same author

Unveiling soil temperature reached during a wildfire event using ex-post chemical and hydraulic soil analysis.

The Science of the total environment·2022
Same author

A comprehensive evaluation of pedotransfer functions for predicting soil water content in environmental modeling and ecosystem management.

The Science of the total environment·2019
Same author

A New Method for Sensing Soil Water Content in Green Roofs Using Plant Microbial Fuel Cells.

Sensors (Basel, Switzerland)·2017

Related Experiment Video

Updated: Feb 6, 2026

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

21.4K

A simple model for estimating changes in rainfall erosivity caused by variations in rainfall patterns.

Gabriel P Lobo1, Carlos A Bonilla2

  • 1Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago, Chile.

Environmental Research
|August 25, 2018
PubMed
Summary

A new statistical equation accurately predicts rainfall erosivity, even with limited precipitation data. This method is crucial for soil loss modeling under changing climate patterns.

Keywords:
CLIGENDaily rainfallErosivityGlobal circulation modelsRUSLESoil loss

More Related Videos

Laboratory and Field Protocol for Estimating Sheet Erosion Rates from Dendrogeomorphology
07:20

Laboratory and Field Protocol for Estimating Sheet Erosion Rates from Dendrogeomorphology

Published on: January 7, 2019

8.2K
Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
08:09

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

12.3K

Related Experiment Videos

Last Updated: Feb 6, 2026

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

21.4K
Laboratory and Field Protocol for Estimating Sheet Erosion Rates from Dendrogeomorphology
07:20

Laboratory and Field Protocol for Estimating Sheet Erosion Rates from Dendrogeomorphology

Published on: January 7, 2019

8.2K
Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
08:09

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

12.3K

Area of Science:

  • Hydrology
  • Soil Science
  • Climate Science

Background:

  • Coupling soil loss models with Global Circulation Model (GCM) precipitation forecasts is challenging due to differing time resolutions.
  • Scaling down GCM precipitation data often ignores rainfall intensity distribution, a key factor in soil erosion.

Purpose of the Study:

  • Develop a statistical equation to compute event-based rainfall erosivity using minimal data.
  • Address the challenge of varying time resolutions between GCMs and soil loss models.

Main Methods:

  • Developed an empirical equation using total precipitation (P) and maximum 0.5-hour rainfall intensity (I₀.₅).
  • Calibrated the equation with measured precipitation data from 28 sites in Central Chile.
  • Tested the equation with simulated rainfall patterns from the CLIGEN weather generator and varying time resolutions (1–24 hours).

Main Results:

  • The equation achieved R² values of 0.99 when using high-resolution intensity data (I₀.₅).
  • Recalibrated equation using 1–24 hour intensities yielded R² values from 0.78 to 0.99.
  • Higher data resolution consistently improved erosivity estimation accuracy.

Conclusions:

  • The developed equation is robust for predicting rainfall erosivity under changing precipitation patterns, including climate change scenarios.
  • The method offers a practical solution for integrating GCM precipitation data into soil loss models.
  • The study highlights the importance of rainfall intensity and data resolution for accurate erosivity estimates.