| Title: | Improves the Interpretation of the Standardized Precipitation Index Under Changing Climate Conditions |
| Version: | 0.2.0 |
| Description: | Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/gabrielblain/SPIChanges |
| BugReports: | https://github.com/gabrielblain/SPIChanges/issues |
| Depends: | R (≥ 3.5) |
| Imports: | gamlss, gamlss.dist, lubridate, MuMIn, rlang, spsUtil, stats, brglm2, zoo |
| Suggests: | archive, curl, doParallel, dplyr, foreach, ggplot2, grid, knitr, ncdf4, patchwork, RColorBrewer, rmarkdown, roxyglobals, sf, purrr, testthat (≥ 3.0.0), tidyr |
| VignetteBuilder: | knitr |
| Config/roxyglobals/filename: | globals.R |
| Config/roxyglobals/unique: | FALSE |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-US |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-06-06 11:20:34 UTC; Gabriel |
| Author: | Gabriel Constantino Blain
 [aut, cre],
Graciela R. Sobierajski
[aut],
Leticia L. Martins
[aut],
Adam H. Sparks
[aut] |
| Maintainer: | Gabriel Constantino Blain <gabriel.blain@sp.gov.br> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-06 11:40:02 UTC |
Daily Rainfall Amounts
Description
Daily rainfall amounts recorded in millimetre in Campinas state of Sao Paulo, Brazil.
Usage
CampinasRain
Format
CampinasRain
A dataframe with 2 columns and 16071 rows:
- Date
Date
- Rain
Daily rainfall amounts recorded in millimetre in Campinas state of Sao Paulo, Brazil
...
Source
Changes in Drought Events in Brazil (1980-2024)
Description
This dataset provides the changes (in percentage) in Moderate to Extreme, Severe to Extreme, and Extreme Drought events across Brazil
for the four seasons (Summer, Autumn, Winter, and Spring) over the period 1980–2024.
The data were generated using the SPIChanges() function applied to rainfall data from NOAA PSL.
Usage
Map
Format
Map
A matrix with 2841 rows and 18 columns:
- lon
Longitude in decimal degrees
- lat
Latitude in decimal degrees
- SummerModerate
Percentage change in moderate drought events in Summer
- SummerSevere
Percentage change in severe drought events in Summer
- SummerExtreme
Percentage change in extreme drought events in Summer
- SummerModel
The selected gamma-based model in Summer
- AutumnModerate
Percentage change in moderate drought events in Autumn
- AutumnSevere
Percentage change in severe drought events in Autumn
- AutumnExtreme
Percentage change in extreme drought events in Autumn
- AutumnModel
The selected gamma-based model in Autumn
- WinterModerate
Percentage change in moderate drought events in Winter
- WinterSevere
Percentage change in severe drought events in Winter
- WinterExtreme
Percentage change in extreme drought events in Winter
- WinterModel
The selected gamma-based model in Winter
- SpringModerate
Percentage change in moderate drought events in Spring
- SpringSevere
Percentage change in severe drought events in Spring
- SpringExtreme
Percentage change in extreme drought events in Spring
- SpringModel
The selected gamma-based model in Spring
...
Source
Generated using the SPIChanges() function applied to rainfall data obtained from NOAA PSL (https://psl.noaa.gov/).
Daily Rainfall Amounts
Description
Daily rainfall data from Oxford-UK (Radcliffe Observatory). Covers the period from January 1827 to January 2020. Belongs to Burt, Stephen (2020). Daily meteorological data from the Radcliffe Observatory (now Radcliffe Meteorological Station), from January 1815: updated to January 2020. figshare. Dataset.
Usage
OxfordRain
Format
A data frame with 70523 rows and 1 column:
- Rain
Precipitation in millimeters
Source
<doi.org/10.6084/m9.figshare.11956239.v1>
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
|
only.linear |
A character string value ( |
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")
Aggregates daily rainfall totals at quasi-week time scales
Description
Aggregates daily rainfall totals at quasi-week time scales
Usage
TSaggreg(daily.rain, start.date, TS = 4L)
Arguments
daily.rain |
Vector, 1-column matrix or data frame with daily rainfall totals. |
start.date |
Date at which the aggregation should start. Preferred formats are “YYYY-MM-DD”, “YYYY/MM/DD” but most any valid date format should work. |
TS |
Time scale on the quasiWeek basis (integer values between 1 and 96). Default is 4, which corresponds to the monthly time scale. |
Details
This package adopts a quasi-weekly time step, dividing each month into four fixed periods:
days 1–7, 8–14, 15–21, and 22 through the end of the month. This approach ensures a
consistent total of 48 quasi-weekly intervals per year, regardless of the selected
aggregation time scale (TS).
For instance, when TS = 4 (representing a one-month backward-looking window), the
function computes cumulative precipitation totals over four consecutive quasi-weekly
periods. This aggregation is repeated across all 48 periods of each year, generating
a time series of aggregated precipitation values suitable for subsequent SPI estimation.
Value
A matrix with rainfall amounts aggregated at the time scale selected by the user
Examples
daily.rain <- CampinasRain[,2]
rainTS4 <- TSaggreg(daily.rain=daily.rain,start.date="1980-01-01",TS=4)
Coordinates for Entire Brazil
Description
This dataset provides coordinates for Brazil at a spatial resolution of 0.5 x 0.5 degrees.
Usage
lonlat
Format
lonlat
A matrix with 2841 rows and 2 columns:
- lon
Longitude in decimal degrees
- lat
Latitude in decimal degrees
...
Source
Data generated using information from the Instituto Brasileiro de Geografia e Estatística (IBGE). For more information, visit: https://www.ibge.gov.br/
Rainfall Aggregated at the 4-quasi week time scale
Description
Rainfall amounts aggregated using TSaggreg(). Campinas state of Sao Paulo, Brazil.
Usage
rainTS4
Format
rainTS4
A matrix with 4 columns and 576 rows:
- Year
Year
- Month
Month
- quasiWeek
Four quasi-weekly periods in each month
- rain.at.TS4
Rainfall amounts aggregated at TS equal to four
...