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Modified AR Scale for Flood Impacts
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Created: May 13, 2025 at 10:41 p.m.
Last updated: May 21, 2025 at 5:39 p.m.
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Abstract

Atmospheric rivers (ARs) are key drivers of regional water supply and flood risk in subtropical and mid-latitude regions, generating interwoven beneficial and hazardous impacts. The original AR scale, developed for early-warning communication, ranks ARs from 1 (“primarily beneficial”) to 5 (“primarily hazardous”) based on atmospheric vapor transport. However, the AR scale does not account for physical processes on the land surface that can strongly influence flood response. Analyzing over 70,000 AR events across 142 catchments in California and central Chile, here we show that runoff efficiency, primarily controlled by antecedent soil moisture, is the dominant source of peak streamflow variability not explained by the AR scale. Based on this insight, we present a modified AR scale for flood impacts that incorporates antecedent moisture conditions. This modification doubles the scale’s correspondence with peak streamflow and increases the number of floods classified as hazardous by over 25%, raising AR flood detection rates to 87% in California and 72% in central Chile. These findings demonstrate that incorporating critical land surface conditions into hazard classification can enhance early-warning tools for communicating not just hazard presence, but likely impact.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
42.1440°
East Longitude
-69.7690°
South Latitude
-40.3265°
West Longitude
-124.1170°

Temporal

Start Date:
End Date:

Content

README.md

Modified AR Scale Analysis

This repository contains the code, data, and helper functions used to reproduce the statistical analyses and figures in the manuscript Antecedent Moisture Enhances Early Warning of Atmospheric River Flood Impacts, submitted to Nature Communications.

Directory Structure

  • mod_ar_scales/
  • mod_ar_scale.Rproj - RStudio project file
  • code/
    • analysis/ - Main analysis scripts (01-04)
    • figures/ - Scripts to generate publication figures
    • ar_scale_helper_func.R - Shared helper functions and package loading
  • data/
    • source_data/ - Preprocessed inputs (event-level and daily time series)
    • intermediate_data/ - Outputs from each analysis step used downstream
    • result_data/ - Final curated results for figures and manuscript
    • mapping_data/ - Shapefiles and spatial data for mapping
  • figures/ - Exported figure PNGs for publication

1. System Requirements

Operating System

  • Tested on macOS Sequoia 15.4.1 (Apple M1 Max, 64 GB RAM)
  • Expected to work on Unix-like systems (macOS, Linux); minor path changes may be needed for Windows

Software Dependencies

  • R version: 4.2.2
  • RStudio version: 2024.04.2+764 ("Chocolate Cosmos")

R Packages Required

  • broom
  • colorspace
  • cowplot
  • data.table
  • dataRetrieval
  • glmtoolbox
  • lubridate
  • parallel
  • pbmcapply
  • performance
  • purrr
  • relaimpo
  • sf
  • slider
  • spData
  • tidyverse
  • wCorr

Install with: r install.packages(c("broom", "colorspace", "cowplot", "data.table", "dataRetrieval", "glmtoolbox", "lubridate", "parallel", "pbmcapply", "performance", "purrr", "relaimpo", "sf", "slider", "spData", "tidyverse", "wCorr"))

2. Installation Guide

Instructions

  1. Download or clone the repository.
  2. Open mod_ar_scale.Rproj in RStudio to automatically set the project working directory.
  3. Install all required R packages listed above.

Typical Install Time

  • ~10 minutes on a standard desktop with internet access

3. Demo

How to Run

  1. Source code/ar_scale_helper_func.R to load all required packages and helper functions.
  2. Run analysis scripts in the following order:
    • code/analysis/01_original_scale_analysis.R
    • code/analysis/02_asm_proxy_variable_selection.R
    • code/analysis/03_modified_ar_scale_creation.R
    • code/analysis/04_modified_scale_analysis.R

Expected Output

  • Intermediate outputs written to data/intermediate_data/
  • Final data outputs written to data/result_data/
  • Figure input-ready data generated for use by figure scripts

Expected Runtime

  • ~45 min on a modern desktop (Apple M1 Max, 64 GB RAM)

4. Instructions for Use

To apply this analysis to a new dataset:

  1. Format inputs to match the structure in data/source_data/
  2. Replace or add your own shapefiles to data/mapping_data/ as needed
  3. Run the analysis scripts to produce modified AR scale data

5. Reproduction Instructions

To reproduce all manuscript results and figures:

  1. Run all four scripts in code/analysis/ to generate intermediate and result datasets
  2. Then run scripts in code/figures/ to generate each figure individually:
  3. Figure1.R, Figure2.R, ..., Figure7.R
  4. Output figures are saved to the figures/ folder

Note: All scripts depend on code/ar_scale_helper_func.R for packages and custom functions.

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Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
U.S. National Science Foundation Graduate Research Fellowship Program (GRFP) Grant No. 1937966

How to Cite

Webb, M. (2025). Modified AR Scale for Flood Impacts, HydroShare, http://www.hydroshare.org/resource/cee30237618d48e8bb0d09ecff9a4a7c

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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