SPIChanges {SPIChanges}R Documentation

Detect trends and quantify their effect on the probability of SPI values occurring

Description

Detect trends and quantify their effect on the probability of SPI values occurring

Usage

SPIChanges(rain.at.TS, only.linear = "Yes")

Arguments

rain.at.TS

A 4-column matrix generated with TSaggreg(). No other objects are accepted.

  • 1st column is years (YYYY),

  • 2nd is the months (1 to 12),

  • 3rd is the quasiWeeks (1 to 4),

  • and 4th is the rainfall totals accumulated at a time scale.

only.linear

A character string value (Yes or No) defining if the function must consider only linear models (Yes) or linear and non-linear models (No). Default is Yes.

Details

The SPIChanges() function implements a nonstationary parametric approach to detect changes in precipitation patterns and assess their impact on the expected frequency of Standardized Precipitation Index (SPI) values. It evaluates 16 candidate models based on time-varying gamma distributions, which account for a broad range of linear and nonlinear changes in both the mean and dispersion of the precipitation series.

Model selection is performed using the second-order Akaike Information Criterion (AICc), and the selected model is used to compute the cumulative probability of each precipitation amount under changing climate conditions. These nonstationary probabilities are then compared with those from the original, stationary SPI algorithm to identify whether the frequency of drought events has increased or decreased over time.

For detailed explanations of the gamma-based models and the model selection procedure, please refer to the README and Vignettes included in the package.

Value

A list object with:

data.week

The Rainfall amounts, SPI, cumulative probability of the SPI values under the stationary approach, cumulative probability of the SPI values under the non-stationary approach, and the changes in the frequency of below zero SPI values caused by the changes in rainfall patterns.

model.selection

The generalized additive model that best fits the rainfall series

Changes.Freq.Drought

changes in the frequency of zero precipitation, moderate to extreme, severe to extreme and extreme drought events,as categorized by the SPI classification system, caused by the changes in rainfall patterns. Changes in the precipitation amounts associated describing normal conditions is also shown.

Statistics

Year to year changes in the expected frequency of moderate to extreme, severe to extreme and extreme drought events.

data.week

The Rainfall amounts, SPI, cumulative probability of the SPI values under the stationary approach, cumulative probability of the SPI values under the non-stationary approach, and the changes in the frequency of below zero SPI values caused by the changes in rainfall patterns.

model.selection

The generalized additive model that best fits the rainfall series

Changes.Freq.Drought

changes in the frequency of zero precipitation, moderate, severe and extreme drought events, as defined by the SPI classification system, caused by the changes in rainfall patterns. Changes in the precipitation amounts associated describing normal conditions is also shown.

Statistics

Year to year changes in the expected frequency of moderate, severe and extreme drought events.

Examples


rainTS4 <- rainTS4
Changes_SPI <- SPIChanges(rain.at.TS=rainTS4, only.linear = "yes")

[Package SPIChanges version 0.2.0 Index]