Antecedent moisture enhances early warning of atmospheric river flood hazards

Domain: neuroscience
Published: 2026-02-12
Editorial outcome: minor_revision

Abstract
Atmospheric rivers are narrow bands of water vapor transport and serve as key drivers of water supplies and flood hazards in mid-latitude regions. The atmospheric river scale supports early-warning communication by ranking events from potentially beneficial to hazardous based on atmospheric forcing represented by water vapor transport magnitude and duration. However, the scale does not consider land-surface conditions that can influence how precipitation derived from water vapor translates into streamflow and flood hazards. Analyzing atmospheric river landfalls across catchments in California and central Chile, we show that divergences between atmospheric river rank and flood response are primarily explained by pre-existing soil moisture conditions. Based on this insight, we develop a simple modification to the atmospheric river scale that nearly doubles the scale’s correspondence with peak streamflow and increases the number of flood-generating atmospheric rivers classified as hazardous by more than 30%. These findings demonstrate that incorporating land-surface conditions can enhance early-warning hazard classification tools.

Introduction
Atmospheric rivers (ARs) are transient corridors of concentrated water vapor movement in the lower atmosphere1. The mean daily moisture transport of an AR is more than double that of the Amazon River2. Upon landfall, ARs play a vital role in replenishing regional water supplies, delivering 15–55% of mid-latitude precipitation3–6, supplying 22% of global runoff7, ending droughts8, and recharging groundwater aquifers9. However, some ARs also produce damaging hydrologic hazards, of which flooding is the most frequent and costly. ARs are responsible for up to 80% of floods in many mid-latitude and subtropical regions7,10,11 and cause an estimated $1.1 billion (USD) in annual damages on the United States (U.S.) West Coast alone12. The capacity of ARs to refill reservoirs one day and flood nearby homes the next day makes accurate and timely communication of likely AR flooding hazards essential for water resource planners, forecasters, and emergency managers.

Translating potential AR hydrologic hazards into actionable guidance begins with hazard classification frameworks that relate storm characteristics to likely impacts. The widely used U.S. West Coast AR scale13 ranks ARs from 1 (“primarily beneficial”) to 5 (“primarily hazardous”) based on integrated vapor transport (IVT) magnitude and duration. Because IVT is predictable at longer lead times than precipitation14, the AR scale supports early hazard assessment and communication, complementing more detailed forecast models that often require multiple inputs and local calibration. However, some high-ranked ARs produce no substantial flooding while some lower-ranked ARs cause severe floods15,16. While higher AR ranks are generally associated with greater flood damages and more frequent weather advisories, ARs of similar rank can produce widely varying hydrologic impacts12,17. These examples suggest that the AR scale may need to consider variables beyond water vapor transport to account for the complex drivers of AR flooding.

Several physical processes not captured by AR IVT magnitude and duration can shape how a landfalling AR translates into streamflow. One key factor is AR precipitation efficiency, which refers to the fraction of AR water vapor converted into precipitation18. Although precipitation is positively correlated with IVT19, precipitation efficiency can vary with AR orientation20,21, atmospheric instability22, barrier jet position23, air temperature24, and water vapor height25. Another factor of comparable importance to AR flood generation is runoff efficiency, defined as the fraction of event-total AR precipitation transformed into stormflow26. While runoff efficiency is a critical control on flood response across many storm types27–29, it plays an especially important role during ARs, which can generate nearly twice the runoff of non-AR storms with similar precipitation inputs30. Runoff efficiency is modulated by antecedent soil moisture16,18,31, snowpack16,32, precipitation phase18,33, and catchment characteristics such as slope, soil texture, and disturbance history34. The magnitude of the resulting peak streamflow reflects contributions from AR-generated stormflow (produced by AR precipitation efficiency and runoff efficiency) and existing baseflow conditions, defined as the portion of streamflow volume determined by groundwater and slow drainage. Baseflow varies on timescales of weeks to months, and elevated values can further amplify flood potential35,36. Although AR precipitation efficiency, runoff efficiency, and baseflow conditions are known to modulate streamflow response, their combined roles in shaping streamflow variability during ARs, and how they influence the AR scale’s ability to represent flood hazards, remain unclear.

The current widely used AR scale is fundamentally a measure of the strength of an AR over a given region, based on atmospheric forcing represented by water vapor transport. Emerging evidence that AR flood response depends on both atmospheric and land-surface conditions indicates that translating AR strength into potential flood hazard likely requires additional information beyond IVT magnitude and duration. Recent studies have focused on refinements to the AR scale based on atmospheric conditions, for example, by applying regional interpretations of benefits and hazards37,38, shifting the lower-bound IVT thresholds for polar environments39, and incorporating indicators of precipitation efficiency40. However, no research to date has systematically addressed how to refine the AR scale to reflect flood hazards based on land-surface conditions. Incorporating variables that represent key land-surface conditions into the AR scale could bridge the gap between early-warning capabilities and process-based hydrologic insights into AR flood generation, there by improving overall AR flood preparedness and response.

We hypothesize that AR-driven flood variability is shaped not only by water vapor transport but also by three critical physical processes that govern how water vapor translates into streamflow: AR precipitation efficiency, runoff efficiency, and baseflow conditions. We further hypothesize that incorporating indicators of these processes into the AR scale could improve correspondence between the AR scale and flood magnitudes. To test our hypotheses, we analyze 70,585 AR landfalls from 1950 to 2023 across 142 catchments (see Methods for detailed selection criteria) spanning the U.S. state of California and central Chile, two mid-latitude regions with strong AR influences and diverse hydroclimatic settings (Supplementary Fig. 1). This cross-hemispheric approach enables evaluation of the AR scale within and outside the western U.S. domain where the scale was initially developed and deployed. We identify AR landfalls using the Global AR Database41 based on ERA5 reanalysis42. Climatic variables, including precipitation, snow water equivalent (SWE), and soil moisture, are derived from ERA5-Land43 to characterize atmospheric and land-surface conditions. Next, we quantify AR event flood response using peak daily streamflow occurring during or within two days after the AR precipitation event, measured at long-term (at least 20 years of records between 1950 and 2023) gauging stations within ±2° latitude (approximately half the width of the median AR44) of the AR landfall. Flood events are defined as instances where peak streamflow exceeds the catchment-specific mean seasonal maximum flood magnitude. We then apply statistical methods, including distributional comparisons, correlation, and variance decomposition, to evaluate the relationship between AR rank and flood response, disentangling the influence of land-surface conditions.

Our analyses reveal that in California and central Chile, much of the flood response variability not captured by the IVT-based AR scale arises from differences in runoff efficiency influenced by antecedent moisture. Building on this process-based understanding, we present a physically grounded and operationally compatible modification to the AR scale that combines AR IVT with catchment-scale antecedent moisture conditions. This simple modification provides a stronger indication of AR flood hazard potential and more reliably flags flood-generating ARs, improving the value of the scale for anticipating and communicating flood hazards in mid-latitude, AR-influenced regions. More broadly, our findings demonstrate that incorporating land-surface conditions into hazard classification tools can enhance the capacity of early-warning systems to communicate the potential magnitude of hazards.

Results
Assessing the ability of the AR scale to characterize flood hazards in California and central Chile
ARs are the dominant source of precipitation and floods in both California and central Chile during the wet months of the cool season (October–April in California; April–October in Chile). Unless otherwise noted, median values are reported followed by the interquartile range (IQR) in parentheses. For the AR events analyzed, the median AR duration is 36 h (22–63 h) in California and 34 h (19–56 h) in central Chile, with median event maximum IVT of 632 kg m−1 s−1 (512–773 kg m−1 s−1) and 593 kg m−1 s−1 (448–739 kg m−1 s−1), respectively (Supplementary Fig. 2). Event-level characteristics for all 70,585 ARs analyzed in this study are provided in Supplementary Data 2. To evaluate the hydrologic hazard, we characterize each AR by its peak streamflow magnitude, a metric central to flood frequency analysis and infrastructure design thresholds. Peak streamflow (Qmax) is reported throughout in normalized units by dividing by the mean seasonal maximum flood during the cool season, with values equal to or greater than 1 classified as floods. ARs account for 92% and 85% of all cool-season floods in California and central Chile, respectively (Supplementary Fig. 3). AR contributions to flooding vary spatially, at high elevations snow accumulation can delay or decouple AR precipitation from streamflow response16,45, whereas many lower-elevation catchments show near-total attribution. Despite their dominant role in flood generation, only 5% of all landfalling ARs in California and central Chile lead to floods. This asymmetry, previously documented in the western U.S11 and now extended to the Southern Hemisphere, suggests that anticipating flood hazards requires going beyond AR detection to understand what makes some ARs more hazardous than others.

The AR scale classifies AR hazard potential solely based on water vapor transport magnitude and duration, potentially overlooking weaker flood-generating ARs. The AR scale identifies 63% of all flood-generating ARs in California and 47% in central Chile as primarily or mostly hazardous (AR 4–5; Fig. 1a). However, most high-ranked ARs do not produce flooding, with only 17% of AR 4–5 events in California and 15% in central Chile generating flood responses. Conversely, weaker ARs with lower water vapor transport (AR 1–2) contribute 12% of all cool-season floods in California and 22% in central Chile. The range of observed peak streamflow can vary widely within each AR scale rank and shows substantial overlap with adjacent ranks (Fig. 1b). Wilcoxon rank-sum tests for each catchment find that the median percent of adjacent AR ranks with significant separation of flood response (hereafter referred to as the median separation of flood response) is 20% (20–40%) in California and 40% (20–60%) in central Chile. Even where statistically significant hydrologic separation is detected, the median Hodges-Lehmann difference in normalized peak streamflow between adjacent AR ranks is small across catchments (0.018, IQR: 0.011–0.031 in California and 0.037, IQR: 0.025–0.054 in central Chile), further indicating that the AR scale does not meaningfully separate ARs by peak streamflow response. Full catchment-level statistical test results are provided in Supplementary Data 1.

Catchment-level Spearman rank correlations between interpolated AR rank and peak streamflow are modest, with median values of ρ = 0.29 in both California (0.24–0.34) and central Chile (0.22–0.34; Fig. 2a). In contrast, nearly all catchments report higher AR scale correlations with event-total precipitation compared to streamflow, although these correlations are still modest, with median values of ρ = 0.37 (0.33–0.40) in California and ρ = 0.42 (0.39–0.43) in central Chile (Fig. 2b). This indicates that the AR scale is more representative of meteorological response than hydrologic response. The correlation differences vary with mean catchment temperature (Fig. 2c), pointing to the importance of catchment-specific conditions (e.g., snow accumulation and available storage) in determining the timing and magnitude of AR-driven flood response.

Collectively, these results show that the AR scale provides limited insight into the peak streamflow response and flood hazard potential of ARs in mid-latitude, AR-influenced regions. Flood-generating ARs occur across the full range of AR strengths, and ARs of similar rank often yield highly variable peak streamflow. This can cause the AR scale to overestimate risk from high water vapor transport ARs that do not cause floods or overlook lower water vapor transport ARs that produce damaging floods. Given that AR scale rank alone does not consistently explain peak streamflow response, we next examine the physical processes that mediate how AR water vapor is converted into peak streamflow.

Runoff efficiency and antecedent soil moisture explain peak streamflow response not captured by the AR scale
To examine why AR scale rank and flood response can diverge in mid-latitude regions, we first quantified the portion of peak streamflow variability beyond what AR rank alone explains. For each catchment, we fit a generalized linear model with interpolated AR rank as the sole predictor of peak streamflow, with model residuals between observed and predicted peak streamflow representing the peak streamflow variability not explained by AR scale rank. We then applied variance decomposition using the Lindeman, Merenda, and Gold method46 to attribute the residual peak streamflow variability to three physically grounded processes that link AR water vapor transport to streamflow, namely AR precipitation efficiency, runoff efficiency, and baseflow conditions (see Methods). These predictors were derived from ERA5-Land43 and streamflow data, with baseflow estimated using the Lyne-Hollick filter47.

Together, these three processes resolve the majority of residual peak streamflow variability not explained by the AR scale, accounting for a median 86% (78–89%) of residual variability in California and 82% (75–85%) in central Chile. Individually, runoff efficiency explains a median 41% (36–45%) of residual peak streamflow variability in California and 37% (33–42%) in central Chile (Fig. 3a) and is the top explanatory process in approximately 85% of catchments. AR precipitation efficiency and event baseflow conditions play a smaller role, with each accounting for approximately 20–25% of residual peak streamflow variability. Spatial patterns align with regional hydroclimate, with AR precipitation efficiency as the top explanatory process in east-west oriented catchments in California’s Transverse Ranges and baseflow conditions most important in cold, high-elevation catchments (Fig. 3b).

Recognizing runoff efficiency as the main process through which AR rank diverges from peak streamflow in mid-latitude, AR-influenced regions, we examined the hydrometeorological controls that modulate it. Selected for their physical relevance, we tested antecedent soil moisture, snowmelt, and precipitation phase (all derived from ERA5-Land; see Methods). Correlation analysis revealed that antecedent soil moisture (represented using the 7-day mean total-column soil moisture) is the leading control on runoff efficiency in 78% of Californian and central Chilean catchments (Fig. 3c). Snowmelt and precipitation phase hydrometeorological controls on runoff efficiency are dominant mainly in cold, high-elevation climates and rain-snow transition zones. Taken with the variance decomposition, these results indicate that in rain-dominated mid-latitude catchments, antecedent soil moisture primarily governs how efficiently AR water vapor transport is converted to stormflow, which in turn determines when AR rank underpredicts or overpredicts flood magnitude.

Antecedent soil moisture has been shown to influence flood generation through established hydrologic mechanisms. Wet soils reduce storage capacity and infiltration, enhance overland and lateral subsurface flow, and consequently increase the fraction of precipitation converted to stormflow48–50. Since ARs already yield runoff efficiencies two times larger than non-AR storms for the same precipitation input30, changes in antecedent soil moisture conditions have an outsized influence on AR flood potential. Prior work shows that when antecedent soil moisture exceeds a catchment-specific saturation threshold, peak streamflow can increase two-to fourfold among storms of the same AR rank15. Our results build on this body of work by demonstrating that runoff efficiency, governed by antecedent moisture, is a missing component that helps explain departures between AR scale rank and peak streamflow response in mid-latitude, AR-influenced regions. This land-surface process is not currently represented in the original IVT-based AR scale, explaining why some high-ranked ARs do not produce flood peaks while some lower-ranked ARs do. These findings offer an opportunity to strengthen AR flood early warning and communication by incorporating physically grounded insights that reflect how land-surface conditions shape peak streamflow response.

A modified AR scale incorporating antecedent moisture improves flood classification in California and central Chile
To improve alignment of the AR scale with flood response, our goal was to incorporate a simple modification based on a single, widely available variable representing antecedent soil moisture, while maintaining the simplicity and operational compatibility of the original AR scale. Sensitivity testing of multiple antecedent soil moisture proxies and accumulation periods ranging from days to months revealed that a seasonally adjusted 90-day Standardized Precipitation Index (SPI51), calculated using rolling sums from the full cool-season climatology and hereafter referred to as the Seasonal Standardized Precipitation Index (SSPI), provides a simple and consistent enhancement to the AR scale across catchments (Supplementary Figs. 4, 5). There is precedent for using SPI as a proxy for soil moisture, with studies showing that SPI at monthly to seasonal timescales closely tracks soil moisture anomalies52,53. Including all 90-day periods ending within the cool season in the reference distribution captures seasonal to sub-seasonal variability in antecedent moisture while preserving seasonal context. SSPI characterizes cumulative antecedent precipitation anomalies relative to local climatology, requires only precipitation data, and is a practical metric for identifying abnormally wet or dry conditions across diverse hydroclimatic settings.

Using SSPI, we developed a rule-based adjustment to the original AR scale (Fig. 4). This modification retains the original structure—classifying ARs based on IVT magnitude and duration—but adjusts the AR rank based on antecedent moisture conditions, which we have demonstrated are essential to determining the flood response of an AR. AR events are promoted by one rank during wet antecedent moisture conditions (SSPI ≥ 0.5) and demoted by one rank during dry conditions (SSPI ≤ −0.5), with intermediate SSPI values resulting in no change (for details on SSPI threshold selection, see Methods). This threshold-based approach aligns with the non-linear relationship between antecedent soil moisture and streamflow generation during AR events15,54, where peak streamflow can sharply increase once a catchment crosses a critical moisture threshold.

Incorporating antecedent moisture into the AR scale improves its ability to classify ARs by flood response in mid-latitude, AR-influenced catchments. Across both regions, more flood-generating ARs are flagged as hazardous, increasing the proportion of flood-generating ARs assigned to the highest AR ranks (AR 4–5) from 63% to 81% in California and 47% to 64% in central Chile (Fig. 5a, b and Table 1). Additionally, 88% (California) and 64% (central Chile) of low-moisture transport ARs previously classified as AR 1–2 but associated with flooding are promoted under the modified scale, reducing misclassification of high-impact hazards.

The SSPI-modified scale also provides a clearer relationship between AR rank and expected peak streamflow, with the median separation of flood response by AR rank more than doubling, reaching 60% (40–80%) in California and 80% (40–80%) in central Chile (Fig. 5c). Median peak streamflow differences between adjacent AR ranks increase by 2.1-fold in California and 1.7-fold in central Chile under the modified scale. These changes reflect not only improved separation between flood and non-flood AR events but also greater consistency within each AR rank, allowing the modified AR scale to serve as a more meaningful indicator of expected peak streamflow. Beyond improving the stratification of flood response across ranks, the modified AR scale also strengthens correlations between interpolated AR scale rank and peak streamflow at our study catchments. Compared to the original AR scale, the SSPI-modified AR scale improves the correlation with peak streamflow to a median ρ = 0.52 in California (0.46–0.58) and ρ = 0.51 in central Chile (0.44–0.53; Fig. 6a), with the greatest catchment-level gains reaching Δρ = 0.42 (Fig. 6b). Only six catchments, primarily located in settings with cooler mean annual temperatures, show little or no improvement in correlation (Fig. 6c).

The cross-hemispheric improvements observed in California and central Chile demonstrate that the modified AR scale captures process-based controls on AR-driven flood response. The strong performance in central Chile, outside the region where the original AR scale was developed, indicates transferability to other mid-latitude, AR-influenced hydroclimatic settings beyond the western U.S. The more modest gains in colder, high-elevation catchments suggest that snow-dominated regions may require AR flood early-warning tools that explicitly account for snow processes at the land surface and will require further investigation. When applied operationally or extended to other AR-influenced areas, the framework presented can be tuned with region-specific SSPI thresholds or adapted using alternative antecedent moisture proxies that best reflect local hydrologic memory. More broadly, this framework provides a structured approach for identifying and integrating key land-surface conditions into hazard early-warning tools for both AR-driven and other precipitation-driven flood hazards across diverse hydroclimatic settings.

A case study from the Big Sur River catchment in California during the 2017 water year illustrates how the modified scale improves alignment with peak streamflow responses (Fig. 7a–c). A strong early-season AR made landfall in mid-October 2016 following a prolonged dry spell. Although the original AR scale classifies the storm as an AR 4, the AR produced limited peak streamflow. Based on antecedent moisture conditions represented with SSPI, the modified scale demotes the AR rank to an AR 3. In contrast, four ARs occurred in February 2017 following sustained wet conditions, including three that are promoted by the modified scale. Notably, a late-February AR originally classified as an AR 3 generated a significant peak streamflow equivalent to an 8-year return interval flood. The modified AR scale promotes this AR to an AR 4, reflecting considerable antecedent moisture and the accompanying increase in flood potential.

Implications for AR flood hazard assessment
The original AR scale classifies ARs based on IVT magnitude and duration, providing a physically meaningful measure of atmospheric forcing. While this framework is modestly well-correlated with precipitation outcomes, it does not account for the influence of land-surface conditions on flood response. Antecedent soil moisture critically shapes runoff efficiency, determining whether the land surface will absorb or release incident precipitation. Many AR studies emphasize AR families or storm sequences55,56, which influence short-term antecedent conditions. Here, we highlight the importance of longer-term (seasonal to sub-seasonal) moisture anomalies that effectively capture the hydrologic memory modulating catchment storage and flood potential.

Building on this physical understanding, we provide an operationally scalable enhancement to the AR scale that better predicts flood hazards in mid-latitude regions. The rank of an AR is promoted or demoted based on whether a catchment is wet or dry going into the storm, linking the atmospheric (IVT) and land-surface (antecedent moisture) conditions that determine AR flood magnitude in rainfall-dominated catchments. This approach preserves the strengths of the original scale, including its simplicity, IVT-based structure, long lead-time availability, and compatibility with forecast systems, while offering enhanced early warning for AR flood hazards. Because the modification requires only gridded precipitation data, it is readily transferable to mid-latitude regions influenced by ARs. Beyond these hydroclimatic settings, the framework presented can be adapted using region-specific thresholds or alternative land-surface proxy variables that capture the dominant controls on flood generation. As AR characteristics change in a warming climate57, such process-informed enhancements will be increasingly crucial for early-warning systems that reflect evolving flood hazards.

More broadly, this work illustrates that hazard classification tools can be enhanced by accounting for the physical processes that determine how a hazard interacts with the land surface. This opportunity extends beyond ARs and flooding to other weather hazards where antecedent land-surface conditions shape hydrologic outcomes, such as snowmelt-driven floods, post-wildfire hazards, or flood hazards exacerbated by antecedent reservoir levels. In a changing climate, where the timing and sequencing of wet and dry periods are shifting, embedding land-surface conditions and process-based understanding into hazard classification will be all the more essential. Doing so bridges the gap between hazard detection and on-the-ground consequences.

In this work, we demonstrate that the ability of the AR scale to identify flood-generating ARs is reduced in cases where antecedent soil moisture alters runoff efficiency, pointing to the importance of land-surface conditions not considered by the scale. Across catchments, incorporating antecedent moisture (represented by 90-day SSPI) into a modified AR scale aligns AR ranks more closely with flood likelihood and substantially improves correlations with peak streamflow. This modification expands the utility of the AR scale for understanding the range of potential peak streamflow outcomes associated with each AR rank, strengthening its usefulness for anticipating and communicating AR-driven flood hazards across diverse hydroclimatic settings. Together, these analyses show that incorporating antecedent moisture enhances the ability of the AR scale to represent flood hazards by providing a more complete picture of how atmospheric forcing translates into hydrologic response.

Methods
Study area
The study domain includes two mid-latitude regions with Mediterranean-type climates: California, U.S. (32–42°N), and central Chile (32–42°S). Both regions are characterized by wet winters, dry summers, and frequent landfalling ARs. California’s topography is dominated by the Sierra Nevada, while central Chile spans the western flank of the Andes Cordillera, from the region of Valparaíso to Los Ríos. These settings provide a natural pairing for cross-hemispheric comparison of AR-driven hydrologic extremes.

Data sources and processing
Daily gauged streamflow observations from 1950 to 2023 were obtained from catchments in the GAGES II database for the U.S58. and CAMELS-CL for Chile59. Streamflow datasets were downloaded from the U.S. Geological Survey (USGS) National Water Information System (waterdata.usgs.gov/nwis) for the U.S. and from CAMELS-CL (camels.cr2.cl) and the Chilean Dirección General de Aguas (DGA; snia.mop.gob.cl/BNAConsultas/reportes) for Chile. Catchments were selected based on the following criteria: (i) greater than or equal to 20 years of daily streamflow records between 1950 and 2023, (ii) geographic location between 32° and 42° latitude in their respective hemisphere, (iii) minimal upstream regulation or diversion (see Supplementary Methods), (iv) spatial independence (i.e., non-nested and non-overlapping), and (v) catchment area greater than or equal to 10 km2. We identified 142 catchments meeting the above criteria (97 in California; 45 in central Chile), spanning hydroclimatic settings (Supplementary Fig. 1).

Daily gridded precipitation, snowfall, SWE, and soil moisture (0–289 cm) for 1950–2023 were obtained from ERA5-Land reanalysis accessed via Google Earth Engine (code.earthengine.google.com) and were spatially aggregated for each catchment. ERA5-Land was selected for its high spatial resolution (~9 km), global availability, and extensive evaluation against observations, making it suitable for cross-hemisphere catchment-scale analyses. Soil moisture data were converted from volumetric to percent maximum units using soil parameters from the FAO-UNESCO Digital Soil Map of the World (DSMW)60.

Gridded data characterizing AR conditions were obtained from the Global AR Database41, which applies the tARget version 4 AR detection algorithm to ERA5 reanalysis to provide global AR footprints at 6 h intervals and ~31 km spatial resolution from 1940 to 2023. We selected this AR database for its global applicability, object-based tracking capabilities, and prior use in AR scale studies37,38. To complement the AR footprint data and provide continuous information on IVT magnitude and duration within each grid cell, we obtained ERA5 hourly IVT from the Copernicus Climate Data Store (cds.climate.copernicus.eu) and resampled these data to 6 h resolution.

AR landfall analysis
Cool-season AR landfalls were identified at each coastal grid cell using the 6 h AR footprint and resampled IVT data described above. Landfall conditions were defined by continuous periods of IVT greater than or equal to 250 kg m−1 s−1 coinciding with the presence of an AR footprint. To distinguish independent AR landfall events, conditions had to persist for at least 12 h, and events separated by gaps longer than 24 h were treated as independent. Each AR landfall event at a given grid cell was then assigned a discrete AR scale rank (AR 1–5) following the original AR scale13 based on maximum IVT (IVTmax) and event duration (ttotal). AR footprints at a given coastal grid cell that did not meet IVTmax or ttotal criteria based on the resampled IVT data were retained and classified as AR 0 events. To facilitate detailed statistical analyses, we calculated an interpolated version of the AR scale (ARcontinuous) as follows:

ARcontinuous = ARdiscrete + 0.5 * (IVTremainder / 250) + 0.5 * (tremainder / 24) (1)

Where ARdiscrete is the original integer AR scale (rank 1–5), IVTremainder is the amount by which the AR’s IVTmax exceeds the lower IVT threshold of its AR scale rank (i.e., the remainder after the division of IVTmax by 250 kg m−1 s−1), and tremainder is the amount by which the AR’s ttotal exceeds the lower duration threshold of its AR scale rank (i.e., the remainder after the division of ttotal by 24 h). The interpolated AR scale gives a decimal weight to how far an AR event’s IVTmax and ttotal values lie from their “surrounding” discrete classification thresholds based on threshold definitions illustrated in Fig. 4.

Precipitation-streamflow event identification
We identified independent cool-season precipitation events for each catchment as contiguous days with more than 5 mm daily precipitation. Precipitation events longer than six days were split if two distinct precipitation maxima were separated by a local minimum (see Supplementary Methods for detailed splitting criteria). Each precipitation event was paired with the peak daily streamflow that occurred during or within two days following the event. We normalized peak streamflow by dividing by the mean seasonal maximum flood for the cool season. The mean seasonal maximum flood for the cool season was selected for normalization because atmospheric river floods predominantly occur during this time of year, providing a reference based on a representative population of atmospheric river-driven floods rather than a mixed flood population that includes snowmelt-driven floods. Events with a normalized peak streamflow greater than or equal to 1 were classified as floods. Precipitation events were excluded if no gauged streamflow observations were available for the corresponding catchment during the event window.

For each precipitation-streamflow event, we calculated the event-total precipitation, precipitation phase (proportion of event-total precipitation falling as snow), AR precipitation efficiency (event-total precipitation divided by IVT magnitude), baseflow conditions (mean value over the seven days preceding the event onset), event-total stormflow (event-total streamflow attributed to event-driven runoff), runoff efficiency (event-total stormflow divided by event-total precipitation), event-total snowmelt, and antecedent soil moisture (mean percent soil saturation over the seven days preceding the event onset). Event baseflow was quantified using the Lyne-Hollick method of baseflow separation47 on daily streamflow with a three-pass filter and standard parameters61 using the R package grwat62.

All cool-season precipitation-streamflow events for a catchment were paired with the highest-ranked landfalling AR occurring within ±2° latitude of the catchment between the onset of precipitation and the date of peak streamflow. This spatial window reflects the typical latitudinal and spatial extent of AR-driven precipitation and streamflow impacts12,13, consistent with AR attribution criteria used in prior studies11,63. Precipitation-streamflow events with no corresponding AR landfall during this window were classified as non-ARs. All precipitation-streamflow events linked to ARs, along with their characteristics, are provided in Supplementary Data 2.

Statistical evaluation of AR scale performance characterizing flood hazards
We evaluated the ability of AR ranks to differentiate peak streamflow responses by examining flood contributions, distributional differences, and catchment-level correlations with streamflow. Given the skewed and non-normal distribution typical of hydrologic data, we used two-tailed, nonparametric tests throughout. First, we quantified the contribution of each AR rank to floods to assess how flood hazard was distributed across the AR scale. To determine whether AR ranks were associated with statistically distinct peak streamflow responses, we conducted Kruskal-Wallis tests64 on all AR event peak streamflow values, grouped by AR rank, for each catchment. For catchments with significant results (p ≤ 0.05), we then applied pairwise Wilcoxon rank-sum tests65 to assess whether adjacent ranks produced distinguishable peak streamflow responses. To quantify the magnitude of these differences, we calculated the Hodges-Lehmann (HL) estimator66 of the median difference in peak streamflow between adjacent AR ranks, providing an estimate of effect size. We assessed the strength of association between the AR scale and hydrologic responses by computing Spearman rank correlation coefficients (ρ) between interpolated AR scale values and both event-total precipitation and peak streamflow at each catchment. Detailed catchment-level results are reported in Supplementary Data 1.

To quantify the portion of peak streamflow variability not explained by the AR scale, we fit generalized linear models using the interpolated AR scale as the sole predictor of peak streamflow for each catchment. A gamma distribution with a log-link was fitted to account for the right-skewed, strictly positive nature of peak streamflow data and to preserve multiplicative relationships between predictors and response variables. For each model, we report key performance metrics in Supplementary Data 1. Model residuals, representing peak streamflow variability not explained by the AR scale, were used to evaluate the influence of additional physical processes on flood response. Variance decomposition analysis at each catchment using the regression-based Lindeman, Merenda, and Gold method46 implemented in the R package relaimpo67 identified the relative contributions of AR precipitation efficiency, runoff efficiency, and baseflow conditions to peak streamflow residuals. Since runoff efficiency was found to be the primary contributor to residual peak streamflow, we identified the dominant hydrometeorological drivers of AR event runoff efficiency by calculating Spearman correlations between runoff efficiency and antecedent soil moisture, precipitation phase ratio, and snowmelt. Further explanations of variable selection for these analyses are provided in the Supplementary Methods.

Developing a modified AR scale for flood hazards
Because runoff efficiency, primarily driven by antecedent soil moisture, emerged as the dominant source of residual peak streamflow variability not captured by AR scale rank, we explored how proxy variables for antecedent moisture could be incorporated into the AR scale’s structure to improve its characterization of flood hazards. Although antecedent soil moisture showed slightly stronger correlations with residual peak streamflow variability at short timescales (1–14 days), it is less suited for operational use due to sparse coverage of in situ networks68 and multi-day latency in global reanalysis products43. Thus, we limited our evaluation to widely available, spatially explicit, and accessible proxy variables. Candidate variables included precipitation-based, IVT-based, and AR frequency-based metrics evaluated over accumulation periods from 1 to 180 days. Among these, 90-day antecedent precipitation showed the strongest correlation with residual peak streamflow variability across both regions (Supplementary Fig. 4).

SSPI (calculated using the R package SEI69) was selected as the antecedent moisture proxy due to its consistent performance across hydroclimatic settings and hydrologic interpretability. SSPI is a standardized index following the SPI framework51, but with a reference Gamma distribution fit to the full historical record of rolling 90-day precipitation totals ending during the cool season, preserving seasonal context while supporting comparisons between years and regions. For each AR event, SSPI was calculated from precipitation over the 90 day period ending two days prior to the start of AR landfall. Additional details are provided in Supplementary Methods. The modified AR scale applied a one-rank promotion or demotion based on threshold values of SSPI. We identified optimal SSPI thresholds by testing a range of values and selecting those that maximized the Spearman correlation between the modified AR scale and observed peak streamflow across catchments (Supplementary Fig. 5), with full threshold selection methods presented in the Supplementary Methods.

To evaluate the SSPI-modified AR scale, we quantified statistical improvements in characterizing flooding relative to the original AR scale across all catchments. Specifically, we applied the same suite of statistical tests used to assess the original scale at each catchment (Kruskal-Wallis tests, Wilcoxon rank-sum tests, HL estimates of median difference, and Spearman correlations) to compare the modified scale’s ability to distinguish flood-generating ARs and stratify peak streamflow response. We also evaluated spatial patterns in the improvement in correlation and tested the robustness of the SSPI-modified AR scale across lead times (Supplementary Fig. 6).

Limitations
Several potential limitations should be considered when interpreting the results of this study. First, our analysis used the Global AR Database41, which applies relatively permissive criteria for AR identification. As shown in prior intercomparison studies, results can be sensitive to the choice of AR detection tools70, with different methods producing variation in event counts, timing, and AR scale rank. We also characterized ARs at landfall, which may not reflect the evolution and impacts of inland-penetrating ARs. Second, we defined floods using peak streamflow. This approach captures streamflow maxima consistently across catchments and is compatible with flood frequency analysis methods, but excludes other dimensions of flood severity such as duration, rate of rise, or volume. Third, while we incorporated antecedent soil moisture from ERA5-Land, this gridded product may not capture the full spatial heterogeneity of soil moisture conditions that can influence runoff and storage. In situ soil moisture observations remain sparse and may not be representative of catchment-scale conditions, restricting their use here. Lastly, climate change introduces non-stationarity that may affect the stability of AR scale classifications. Increases in atmospheric moisture may shift the IVT distribution toward higher values57, necessitating the recalibration of AR scale thresholds. Similarly, the SSPI-modified scale relies on historical precipitation baselines that may shift in a warming climate and may require non-stationary approaches to remain reliable under future conditions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General comments:\nThis is a really good paper, and it has the potential to be a fantastic paper. The authors show that the widely used AR scale\ndoes not adequately capture flooding and propose a modification to the scale based on SPI that captures these hydrologic\nimpacts better. The first portion of the results (“Stronger ARs bring more precipitation, but do not always produce floods”)\nlargely confirms prior literature, but the direct comparison between California and central Chile is a welcome addition. The\nsecond portion (“Runoff efficiency fills gaps between the AR scale and flood response”) uses relative importance\ncontributions to quantify drivers in a way that has not been done before, and the third portion (“A modified AR scale\nincorporating antecedent moisture improves flood classification”) has immediate applications for both academic and\noperational use. The authors are also excellent writers, which made this a pleasure to read.\nI have two broad comments, one technical and one stylistic. The technical one is about the matching of ARs to precipitation and streamflow events. +/-2deg latitude is an extremely wide band – CA is only a span of about ten degrees. Why, when each of the datasets is picked for its high resolution, would you be relying on such a coarse matching heuristic? The\ndefinition of an “AR-driven flood” is critical to this paper’s conclusions. I believe that much more evidence is needed, either\nthrough a literature comparison or further data analysis, to justify the definition as presented. I’m also not entirely convinced\non the definition of a “flood” – what is the variation in the 5-yr RI Qmax, and what other factors contribute to this variation at\nthe catchment scale? Would it be easier to compare flood quantiles directly and remove these sources of variation?\nThe stylistic comment is about the organization. I know Nature Communications prefers a results-first format, but in many\ncases I found that the mention of methods was just enough to raise questions but not enough to answer them. I made note of\nmany of these locations in the specific comments below – I ended up finding most of the answers to my questions later on,\nbut left the comments in place to indicate where in the manuscript I was in want of more information. Please consider either\nadding more detail (incorporating brief methods descriptions within the results) or less detail (removing mentions of specific\nsteps and adding in-text pointers that let the reader know more explanation will be coming later).\nI recommend minor revisions. I look forward to seeing this in publication.\nSpecific comments:\n• Very good abstract\n• Very well cited intro. But the fundamental claim here (runoff efficiency modulates flood risk) is true for many types of storms,\nnot just ARs, and I feel like you need some of that context as well. This paper jumps to mind, but there are many more –\nSharma et al. (https://doi.org/10.1029/2018WR023749)\n• Additionally, any information you could add about why runoff efficiency is uniquely informative for ARs would be a nice\ndifferentiator.\n• Line 71: it’s not clear that “the background level of streamflow” is the definition of baseflow conditions here.\n• Lines 76-79: very strong\n• Line 90: is there a difference between runoff efficiency and AR runoff efficiency? You use both labels\n• Fig S1: How were these particular catchments chosen? Based on gage availability?\n• Line 98: define ECMWF. Also why is the data source for AR landfalls listed, but none of the other datasets are? What about\nprecip, SWE, SM, etc.?\n• Line 100: within 2 degrees latitude is a HUGE spatial range. Justify this a little more. Also plus and minus: what about\nlagged peaks? How do you tie peaks to specific events?\n• Line 129: which ranks qualify as hazardous? Also “just” 69% feels incorrect, that’s almost three-quarters of all ARs\n• Can you somewhere provide statistics on your 70k ARs: mean IVT, mean duration, etc?\n• Line 135: A flow measurement in mm is a measure of runoff, not streamflow; I got very thrown off by the fact that this was\nreferred to as Q. Please mention that this is normalized by catchment, because I originally thought a large portion of the\nvariability could be attributed to catchment size. These numbers also feel large, closer to annual totals than daily totals. A\nmore appropriate metric than normalized flow might be the normalized flow percentile or the return interval of the flow, so\nyou’re comparing like with like across climatologically different regions.\n• Fig S3 (and others): You have a number of NA points plotted in gray, what are those?\n• Fig 2: this is nitpicky, but it’s not Qmax – Ptotal. The notation should be p_Qmax – p_Ptotal\n• Line 177: This sentence feels like it belongs at the beginning of this section, not the end\n• Line 183: missing a lot of detail here. How did you measure the components before variance decomp? How was variance\ndecomp done? What baseflow algorithm?\n• Fig 3: hard to distinguish colors\n• Need a bit more of a transition between Fig 3b and 3c. How did you measure these? Where did the data come from? Why\nthese three factors and not others?\n• Line 215: this information nicely answers my questions about why specifically ARs and runoff efficiency\n• Line 235: Cite McKee et al. (1993) for SPI\n• Can you provide a citation where SPI has been used as a proxy for ASM? Normally SPI is for meteorological conditions. I\nalso want more detail on how you calculated SPI: what distribution? How did you do your seasonal adjustment? Or did you\ndownload it directly from USDM or something like that (strong argument for operational use)?\n• Line 250: that is a lot of additional work condensed into a few lines, well done\n• Fig 4: really like this graphic, I think you distilled it nicely\n• Could you make a confusion matrix? You’re kind of dancing around it, but you have hazardous vs. non-hazardous ARs and\nflood-causing vs. non-flood-causing ARs, and you’ve reported false positives and false negatives without using that\nterminology explicitly. It would be nice to summarize it in a table, and I think it would be helpful to see those numbers\nreported before and after your rank modifications\n• The “greater than 50% of adjacent AR rank pairs with statistically significant differences in Qmax” is a super clunky metric.\nYou’ve described it well, but I just have no sense of what it means in a practical sense. What exactly is it you’re trying to\nconvey to the reader with this? Could you give it your own name to make that more clear, maybe “ability to differentiate\nranks” or “rank organization metric” or something like that?\n• Fig 5a: this was a stacked boxplot in Fig 1a. Could you repeat that here for visual consistency? It wasn’t clear to me at first\nthat these add up to 100%\n• Line 298: where did the 2degC threshold come from? Literature or your own analysis?\n• Can you add the p_Qmax plots from Fig S5 to Fig 2? Similarly, can you add the p_Qmax(new) plots to Fig 6 along with the\ndifference plots?\n• Fig 7: love the case study. I have an issue with the SPI though. If it’s a normal distribution (and you would expect it to be)\nan SPI of +/-5 would have a return period on the order of once every 3.5 million years. I know 2017 was a notable water year,\nbut I‘m suspicious that it’s THAT notable, and to go from -5 to +5 over the course of a few months seems highly unlikely\n• Line 393: CAMELS typo\n• Line 397: Why the >250km from the coast restriction?\n• Line 398: how was spatial independence assessed?\n• Line 418: are ARs assumed to move only horizontally as they move inland?\n• Line 428: I like the AR_continuous, very cool and useful idea. Which statistical analyses is it used for? It’s not clear in the\nresults which tests are run on this vs. the actual ranks\n• Line 442: why do this splitting?\n• Line 448: little more detail on the return interval method?\n• Line 489: Spearman correlations between ASM/phase ratio/snowmelt and… is the Y variable the Qmax residuals? Unclear\nhere\n• Line 508: explain “non-overlapping 90-day intervals.” Is this your seasonal adjustment? What are the start and end dates of\neach interval?\n• I would love a summary table in text to compare the statistical test results of the original vs. modified scale.\n• The data is available, but the code is not. This is not a reproducible product. Please publish, at a minimum, scripts that\nrecreate the main manuscript figures, and ideally the full R workflow. Additionally, please add citations for key packages\nused in this analysis.", "reviewer_2": "Review of ‘Antecedent Moisture Enhances Early Warning of Atmospheric River Flood Impacts’ by Webb et al.\nOverview\nThis paper describes a proposed modification to the Atmospheric River (AR) scale – a scale based on the intensity and\nduration of an AR’s integrated water vapor transport – that includes antecedent moisture to better capture the likelihood that\nan AR will cause flooding impacts. Across watersheds of both California and Chile, the paper documents the limitations of\nthe current scale with respect to flooding impacts. It then analyzes other factors during AR events that contribute to the\nlikelihood of flooding, noting that for most watersheds runoff efficiency is the largest contributing factor, and then devises a\nmethod to incorporate a proxy for runoff efficiency, 90-day Standardized Precipitation Index (SPI), to adjust the AR scale.\nIt then demonstrates that the adjusted AR scale improves the relationship between the scale and the likelihood of flooding.\nThe paper is well written, with sound methods, and addresses a societally relevant challenge. Below I share some specific\ncomments the author should consider to improve the impact and clarity of the paper.\nSpecific comments\nMore emphasis should be given somewhere in the paper why you focused on devising such a simple modification to the AR\nscale, when in theory the linear modeling you did could be used to construct something more complex that represents more of\nthe processes (i.e., precipitation efficiency and baseflow) rather than simply the ‘main’ driver of soil-moisture-controlled\nrunoff efficiency. I think the justification is made in the conclusion (i.e., l. 364-368), but needs to be made sooner (perhaps\neven in the abstract, e.g., ‘goal of a simple, IVT and precipitation-based scale…’).\nL. 154-159: The correlations of the interpolated AR scale with precipitation (~0.4) are indeed greater than its correlation with\nstreamflow (~0.3), but correlations of ~0.4 are still pretty modest. It’s worth noting this.\nL. 163 (figure 2): the color scale range in both panels could be tightened a bit to make the differences across the locations\nmore apparent (i.e., it’s very difficult to distinguish differences in the colors especially in panel b).\nL. 183: the way this subsection title is worded ‘...fills gaps between…’ could be improved; one suggestion is ‘Runoff\nefficiency amends the AR scale to better represent flood response’\nL. 197 (figure 3): Panel C made me very curious why you didn’t consider breaking your data into 2 regimes, rain- and snow-\ndominated. I have a few more comments on this below.\nL. 209-218: Somewhere in this paragraph I think it’s important to note the rain- vs snow-fed distinction. Your results and\nrecommendations are dominated by the rain-fed watersheds.\nL. 211: suggested amended text: “... flood response in these two similar hydroclimates. Specifically, the importance of runoff\nefficiency, and in particular soil moisture, to AR flood…”\nL. 213: suggest “... consequently increase…”\nL. 223-226: If I’m understanding correctly, Figure 3 and the analysis first analyze the impact of runoff efficiency, AR\nprecipitation efficiency, and baseflow conditions, then go on to further separate three contributing factors to runoff efficiency:\nantecedent soil moisture, snowmelt, and precipitation. I find this useful. The sentence on these lines conflates these.\nAlso – ‘Only in a small subset of catchments…” – based on line 191 ~20% catchments are controlled more strongly by other\nfactors, which I’m not sure qualifies as a ‘small subset’ – I suggest you simply quantify this here again.\nL. 231-235: The leap to why a proxy for antecedent moisture – rather than just using soil moisture – is used here is not\nstrongly justified enough.\nL. 240: “... thus is readily available…” (add ‘is’)\nL. 278 and 292-297: The strengthened relationship between the modified AR scale and flooding is statistically significant,\nbut somewhat underwhelming – there is still quite a lot of overlap in the boxplots of Qmax and correlations of ~0.5 are better\nbut still not amazing. I suspect this underwhelming performance is at least in part because of the simplifications made to\npush the modified AR scale toward simplicity – only retaining the runoff efficiency->soil moisture and then further stepping\naway from the physical control by the use of SPI as a proxy.\nL. 344-346: This is somewhat overstated as written.\nL. 353: suggest adding ‘modestly’ i.e., “... modestly well-correlated …”\nL. 364-368: See my comment above – this needs to come through earlier in the paper\nL. 393: CAMELS is misspelled\nL. 495-497: More justification is needed here (references, etc)", "reviewer_3": "The manuscript attempts to extend the existing classification (ranking) scheme of the atmospheric rivers (AR) using proxies\nof the antecedent wetness to more accurately identify flood generating AR-events. Improving physical foundation of AR\nscales is indeed very important for advancing and improving early warning accuracy of the AR-related hazards. The\nproposed adjustment of the AR scale using 90-day antecedent precipitation index has shown potential to improve detection\nof flood prone ARs in the 142 catchments in California and Central Chile.\nHowever, the proposed method is very much tailored to the investigated regions and is likely not to be transferable across\ndifferent locations with different seasonality dynamics of precipitation and evapotranspiration. Moreover, the proposed\nadjustment ignores local catchment characteristics resulting in the variable accuracy of the proposed adjustment of the AR\nscale for different locations. This in turn undermines authors’ claim for advancing early warning of the impacts, which are\nalways location-specific. Most importantly, I see insufficient novelty in the presented approach that merely shows the\nimprovement in predicting flood hazards (but not impacts) using proxies of the antecedent soil moisture. The value of soil\nmoisture for predicting flood magnitudes is well documented and quantified across different climates and scales. Apart\nfrom that, there are several methodological ambiguities and subjective decisions that have to be clarified. Below I present my\ndetailed comments.\nGeneral comments\n1. Effects for the hazard but not impacts: In the title, abstract and in the main text of the manuscript the authors claim that the\nantecedent soil moisture improves early warning of the AR-related flood impacts. I disagree with this statement. The authors\nshowed that accounting for soil moisture improves early warning of AR-related flood peak magnitudes. This is still only a\nhazard component of the risk. In fact, flood magnitudes themselves do not necessary translate into higher impacts, as other\nrisk components might play more crucial role. The implications for the impacts are actually not at all quantified in this study\nand hence no statements regarding the usefulness of the soil moisture for the early warning of the impacts can stem from the\nanalysis performed in this study.\n2. Unclear novelty: Since this study only shows the effectiveness of the antecedent soil moisture in predicting flood\nmagnitude, the novelty of this study is not very clear to me. The authors themselves provide multiple references to the\nstudies that show the crucial role of soil moisture for the corresponding AR-related flood magnitudes. More generally, the\nrole of antecedent soil moisture in modulating runoff efficiency, streamflow and flood magnitudes also for the non-AR related\nevents is well studied across different climates, spatial scales and event sizes, making the novelty of this study rather\nunclear.\n3. Incomprehensive criteria for the classification of AR hazard: Previous studies have shown that antecedent soil moisture\nhas a non-linear effect on runoff generation, i.e., up to a certain soil moisture threshold no streamflow is generated, while\nafter this threshold the streamflow increases rapidly. However, the rate of this increase varies considerable across different\ncatchments based on their specific catchment characteristics. The proposed adjustment of the classification framework does\nnot account for that. This likely explains variable effectiveness of the adjusted AR classification across examined study\ncatchments (Table S2).\n4. Limited transferability of the adjusted AR classification: I commend the authors for examining a wide range of accumulated\nperiods to find a suitable precipitation and IVT-based proxy for antecedent wetness. However, suggested accumulation\nperiods are very likely be only effective in the examined study areas. In regions with different seasonality dynamics of\nprecipitation and evapotranspiration, the proposed accumulation periods might not be optimal. A more objective method for\ndefining the accumulation period for the antecedent soil moisture would very much strengthen the proposed classification\nframework and make it transferable across regions with different seasonal dynamics.\nSpecific comments to the Main\nLine 34: This statement is rather far-fetched, since the actual impacts were not analyzed. See also my general comment.\nLine 55-57: For me as a hydrologist thus portion of the Introduction sounds rather trivial. It has been shown in various studies\nat different scales that extreme precipitation dos not correspond to extreme floods (to name just a prominent few\nhttps://www.nature.com/articles/s41586-019-1495-6 and https://www.science.org/doi/10.1126/sciadv.adl4005).\nLine 72-73: A clarification on the terms “runoff efficiency” and “baseflow conditions” will be very helpful here.\nLine 113: See my earlier comment to Line 34.\nLine 118: I recommend to avoid this acronym, it is not really necessary, only confusing.\nLine 122-123: It is not clear if this is the result (then please add a reference to a corresponding figure) or a statement from\nprevious studies (please add a reference to a corresponding study in this case).\nLine 124-126 As I mentioned in my comment in Line 55-57, the lack of correspondence between extreme precipitation and\nextreme flooding is a universal phenomenon due to the modulation by the antecedent soil moisture. Therefore, there is no\nsurprise that it can be extended to the Southern Hemisphere. See also my general comment regarding the novelty.\nLine 140-143: This analysis ignores intrinsic differences between flood magnitudes across different catchments given\ndifferent level of streamflow persistence (see https://www.pnas.org/doi/full/10.1073/pnas.1311920110). Even when\ndischarge is normalized using catchment area, there are usually huge differences between different catchments (which can\nbe also seen in the data provided in the supporting information of this study). The standard procedure is to standardize the\nstreamflow using either median streamflow or mean maximum annual flood. This can directly affect the results displayed in\nFigure 1.\nLine 176 and elsewhere: hazard instead of risk/\nLine 189: Still not clear what is meant by the runoff efficiency here. This term is also not explained in the Methods section\nlater.\nLine 192: The term precipitation efficiency is also not clear and is not explained in the Method\nsection.\nLine 193 and Figure 3: it is not quite clear to me why baseflow conditions are examined in addition to runoff efficiency. If I\nunderstand correctly that by runoff efficiency the authors actually mean event runoff coefficient, then baseflow level at the\nstart of the streamflow event has a huge effect on runoff efficiency (see\nhttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR022587). Please clarify. I have a similar concern about the lack\nof rational for examining antecedent soil moisture, snowmelt and precipitation phase as potential drivers of runoff efficiency.\nWhy particularly these three? What does precipitation phase actually mean (please explain it in the caption)? Why not the\nvolume of precipitation event is considered as a potential driver of runoff efficiency? Please also clarify in the caption, the\ndefinition for the “dominant driver”.\nLine 209-218: This analysis seems rather cumbersome to me. Why not to test the value of antecedent soil moisture on the\nresiduals of Qmax directly? Please clarify the need for the two-step procedure.\nLine 231-239: I absolutely support the authors idea for look for a soil moisture proxy that can be easily obtained from\nmeteorological data directly, but I do not quite understand why not to use it directly in the analysis in the previous section\ninstead or complimentary to the soil moisture. Please clarify.\nLine 251-255: This part of additionally testing the TIVT along the SPI sounds somewhat post hoc. It would be more helpful\nfirst to outline the requirements for the variable representing soil moisture, state the three options investigated, perform a\nconsistent comparison analysis and then draw a conclusion on which of the option has the highest potential and\napplicability for the early warning of flood-prone ARs.\nLine 265-266: I think it will be very useful to show here not only the mean improvement for all catchments but also indicate\nthe variability of the AR detection across catchments by providing standard deviation.\nLine 268-269 High-hazard events instead of high-impact hazards\nLine 337: AR-driven flood hazard\nLine 337-338, 360, 367-368: The transferability of the proposed framework across different hydroclimatic settings was not\nexamined. In fact, due to selected soil moisture proxies, it is likely that the proposed adjustment is tailored for the two\nexamined regions. Please see also my general comment.\nLine 371, 378 Flood magnitude is still a hazard and cannot be directly translated into the impacts. Please see also several of\nmy comments above.\nSpecific comments to Methods\nLine 395: Deriving 5 years return periods from only 20 years of observation might result in rather uncertain estimates. The\neffect of these uncertainty on the results of this study has to be investigated explicitly.\nLine 440-441: The rationale for the split of precipitation events is not clear to me. Given the size of the analyzed catchments\nthe delay in streamflow response is possible. Please justify this methodological choice. Moreover, the choice of 6 days as\nthreshold for split is also not clear. Please clarify.\nLine 444: I am not quite sure what is meant here by a maximum daily streamflow. Instantaneous daily peak maximum?\nMaximum of the streamflow event? The maximum of all time streamflow time records? If the authors refer to the maximum\nstreamflow occurred during a streamflow event, they first need to clarify how a streamflow event was defined.\nLine 449-450: Floods with return period of 5 years indeed often used as proxy of the overbank flow. However, when\nestimated from only 20 years of observations, this estimate might be very uncertain and might not really represent the\noverbank flow. This needs to be examined. Please see also my comment above.\nLine 453-454: The rationale for selecting particularly these variables for this analysis is also not clarified in the Method\nsection. Please provide the rationale and clarify how exactly each of these variables was derived. Particularly, the definition\nof the event is unclear here. Does it refer to streamflow event? How was it defined? I am also rather confused here by the\nanalysis of baseflow. Baseflow prior to the streamflow event is often used as a proxy of antecedent wetness conditions (see\ne.g. https://doi.org/10.1029/2022WR033226). What does the sum of baseflow during event supposed to represent and why\nshould it be helpful for explaining the variability of respective flood magnitude? Please notice that digital filters for baseflow\nseparation (as the one used in this study) are very poorly related to actual increase in the subsurface flow components\nduring the event (https://doi.org/10.1016/j.jhydrol.2013.09.006).\nLine 499: It is not clear which metrics are meant here, please clarify.\nLine 503-504 It is not clear how this was quantified. Please elaborate.\nLine 512-514 As I mentioned above this analysis sounds very post hoc. See my comment to Line 251-255 and provide a\nconsistent comparison of selected proxies.\nSpecific comments to SI\nFigure S6 The caption is not clear. Please clarify what does “mean” soil moisture means in this context. The description of\nthe precipitation accumulation is also not clear, please improve.\nTable S2 This is not a very useful presentation of the results. It would be much more instructive for the readers, if the authors\nwill show the relationship between the predictive power of the adjusted AR-scale and different catchment characteristics\n(e.g., size, elevation) to shed a light on the variability in performance of the proposed adjusted AR-scale.", "reviewer_1_attached": "General comments:\nThis is a really good paper, and it has the potential to be a fantastic paper. The authors show that\nthe widely used AR scale does not adequately capture flooding and propose a modification to the\nscale based on SPI that captures these hydrologic impacts better. The first portion of the results\n(“Stronger ARs bring more precipitation, but do not always produce floods”) largely confirms\nprior literature, but the direct comparison between California and central Chile is a welcome\naddition. The second portion (“Runoff efficiency fills gaps between the AR scale and flood\nresponse”) uses relative importance contributions to quantify drivers in a way that has not been\ndone before, and the third portion (“A modified AR scale incorporating antecedent moisture\nimproves flood classification”) has immediate applications for both academic and operational\nuse. The authors are also excellent writers, which made this a pleasure to read.\nThank you for your thoughtful review. We are glad you find our work a welcome contribution\nwith immediate applications.\nI have two broad comments, one technical and one stylistic. The technical one is about the\nmatching of ARs to precipitation and streamflow events. +/-2deg latitude is an extremely wide\nband – CA is only a span of about ten degrees. Why, when each of the datasets is picked for its\nhigh resolution, would you be relying on such a coarse matching heuristic?\nThe definition of an “AR-driven flood” is critical to this paper’s conclusions. I believe that much\nmore evidence is needed, either through a literature comparison or further data analysis, to justify\nthe definition as presented. I’m also not entirely convinced on the definition of a “flood” – what\nis the variation in the 5-yr RI Qmax, and what other factors contribute to this variation at\nthe catchment scale? Would it be easier to compare flood quantiles directly and remove these\nsources of variation?\nWe appreciate these insightful comments, as AR flood attribution and the definition of flooding\nare central to this study. We have addressed the specific concerns raised in the line-by-line\ncomments below and the manuscript text.\nThe stylistic comment is about the organization. I know Nature Communications prefers a\nresults-first format, but in many cases I found that the mention of methods was just enough to\nraise questions but not enough to answer them. I made note of many of these locations in the\nspecific comments below – I ended up finding most of the answers to my questions later on, but\nleft the comments in place to indicate where in the manuscript I was in want of more\ninformation. Please consider either adding more detail (incorporating brief methods descriptions\nwithin the results) or less detail (removing mentions of specific steps and adding in-text pointers\nthat let the reader know more explanation will be coming later).\nIn response to your stylistic comment, we have revised the manuscript to include brief\ndescriptions of key procedures within the results and more in-text references to methods where\nadditional full details can be found. These changes are reflected in our line-by-line responses and\nthe revised manuscript text.\nI recommend minor revisions. I look forward to seeing this in publication.\nSpecific comments:\n• Very good abstract\nThank you for the positive feedback.\n• Very well cited intro. But the fundamental claim here (runoff efficiency modulates flood risk)\nis true for many types of storms, not just ARs, and I feel like you need some of that context as\nwell. This paper jumps to mind, but there are many more – Sharma et al.\n(https://doi.org/10.1029/2018WR023749)\nWe agree that runoff efficiency is a broader control on flood magnitude across storm types and\nhave revised the introduction to acknowledge this, citing Sharma et al. (2018), Berghuijis et al.\n(2016), and Ivancic and Shaw (2015) on line 68.\n• Additionally, any information you could add about why runoff efficiency is uniquely\ninformative for ARs would be a nice differentiator.\nTo this point, we have added a reference to prior research noting that ARs are particularly\ninfluenced by runoff efficiency: “While runoff efficiency is a critical control on flood response\nacross many storm types, it plays an especially important role during ARs, which can generate\nnearly twice the runoff of non-AR storms with similar precipitation inputs” on lines 59-62,\nciting Siirila-Woodburn et al. (2023)\n• Line 71: it’s not clear that “the background level of streamflow” is the definition of baseflow\nconditions here.\nTo address this comment and avoid confusion, we have removed this phrase from the manuscript\nand provided an explicit definition of baseflow “defined as the streamflow volume determined\nby groundwater and slow drainage” (line 66).\n• Lines 76-79: very strong\nThank you for the kind words.\n• Line 90: is there a difference between runoff efficiency and AR runoff efficiency? You use\nboth labels\nWe were using both terms to refer to the same concept and have modified the document to only\nuse “runoff efficiency” for clarity and consistency.\n• Fig S1: How were these particular catchments chosen? Based on gage availability?\nWe have added a sentence on lines 89-90 in the main text to clarify where the detailed selection\ncriteria can be found “(see Methods for detailed selection criteria).” Additionally, we have added\nthe following text to the figure legend of Supplementary Figure 1 to clarify how the catchments\nwere selected “Catchments were selected from the GAGES-II and CAMELS-CL data sets based\non data availability, geographic and size criteria, and minimal anthropogenic intervention\ncriteria.”\n• Line 98: define ECMWF. Also why is the data source for AR landfalls listed, but none of the\nother datasets are? What about precip, SWE, SM, etc.?\nFor consistency with the Guan and Waliser 2024 paper that introduces the ERA5-based Global\nAR Catalog (https://doi.org/10.1038/s41597-024-03258-4), we have removed the definition of\nERA5, which would then necessitate the definition of ECMWF. We have also added a sentence\nto the main text on lines 94-96 that states the datasets used for the additional variables: “Climatic\nvariables, including precipitation, snow water equivalent (SWE), and soil moisture, are derived\nfrom ERA5-Land to characterize atmospheric and land surface conditions.”\n• Line 100: within 2 degrees latitude is a HUGE spatial range. Justify this a little more. Why,\nwhen each of the datasets is picked for its high resolution, would you be relying on such a coarse\nmatching heuristic? Also plus and minus: what about lagged peaks? How do you tie peaks to\nspecific events?\nWhile ±2° latitude may seem broad relative to the resolution of the ERA5 dataset, it was selected\nto reflect the typical spatial extent of AR-driven precipitation and streamflow responses. The\nmedian width of an AR is approximately 564 km, or ~5° latitude, based on global climatologies\n(Guan & Waliser 2015). The ±2° window thus captures roughly half the cross-sectional width of\na typical AR. Moreover, ARs often migrate latitudinally during landfall. For example,\nCorringham et al. (2019) found that ARs in their study typically made landfall over a 5-6°\nlatitude range. Additionally, ±2° reflects the synoptic scale of AR-related precipitation and\nstreamflow. This is illustrated in Ralph et al. (2019), where the authors find that ARs making\nlandfall at Bodega Bay on the West Coast of California (located at 38° latitude) are associated\nwith significant precipitation accumulation of 50-100 mm over two days at precipitation gauges\nbetween 32° and 46° latitude. This choice is also consistent with the windows used in prior\nstudies for AR attribution (e.g., Barth et al. (2017): ±2.5°; Barth et al. (2019): ±2.5°; Corringham\net al. (2019): 2.5° cells; Konrad & Dettinger (2017): 2.5° cells; Mahoney et al. (2016): ± 250\nkm).\nTo account for variability in the timing of peak streamflow relative to AR precipitation, we used\nan attribution window that begins with the onset of AR-driven precipitation and extends up to\ntwo days after the end of the AR precipitation event, provided the streamflow peak does not\ncoincide with a subsequent AR precipitation event. This approach allows us to capture the full\nrange of potential runoff response times, which can vary depending on catchment characteristics\nsuch as slope, size, and soil conditions.\nWe have added further explanation and justification of these methodological choices to the\nmanuscript Introduction and Methods (Lines 487-488), including “This spatial window reflects\nthe typical latitudinal migration and spatial extent of AR-driven precipitation and streamflow\nimpacts, consistent with AR attribution criteria used in prior studies.”\n• Line 129: which ranks qualify as hazardous? Also “just” 69% feels incorrect, that’s almost\nthree-quarters of all ARs.\nWe have updated the text to provide more detail on the definition of hazardous, which are AR4-5\nevents that the Ralph et al. 2019 definition of the scale defines as primarily or mostly hazardous.\nWe have also removed “just” next to the percentage.\n• Can you somewhere provide statistics on your 70k ARs: mean IVT, mean duration, etc?\nWe have added the sentence “For the AR events analyzed, the median AR duration is 36 hours\n(22-63 hours) in California and 34 hours (19-56 hours) in central Chile, with median event\nmaximum IVT of 632 kg m-1 s-1 (512-773 kg m-1 s-1) and 593 kg m-1 s-1 (448-739 kg m-1 s-\n1), respectively, and corresponding median event-total precipitation of 34 mm (16-67 mm) and\n47 mm (22-93 mm; Supplementary Figure 2)” on lines 119-122. The full distributions of these\nevent characteristics are shown in Supplementary Fig. 2.\n• Line 135: A flow measurement in mm is a measure of runoff, not streamflow; I got very thrown\noff by the fact that this was referred to as Q. Please mention that this is normalized by catchment,\nbecause I originally thought a large portion of the variability could be attributed to catchment\nsize. These numbers also feel large, closer to annual totals than daily totals. A more appropriate\nmetric than normalized flow might be the normalized flow percentile or the return interval of the\nflow, so you’re comparing like for comparisons across climatologically different regions.\nWe apologize for this confusion. Based on this feedback, as well as similar comments from\nanother reviewer, we have revised our approach to normalize peak streamflow by dividing by\neach catchment’s mean annual flood (MAF), where the annual flood is defined as the maximum\nflow observed in a given year, to calculate a unitless variable. We now define floods as\nprecipitation-streamflow events with a Q ≥ 1, which corresponds to events equal to or larger\nmax\nthan the MAF.\n• Fig S3 (and others): You have a number of NA points plotted in gray, what are those?\nThe gray NA points represent locations where the relationship between variables was not\nstatistically significant at the p ≤ 0.05 level, which is now noted in all relevant figure captions.\n• Fig 2: this is nitpicky, but it’s not Qmax – Ptotal. The notation should be p_Qmax – p_Ptotal\nWe appreciate your attention to this detail. We have corrected the notation in both Figure 2 and\nFigure 6.\n• Line 177: This sentence feels like it belongs at the beginning of this section, not the end\nWe have moved the sentence to the beginning of the section to provide background on our\nchoice of flood metric and improve the logical flow of the text (Line 124).\n• Line 183: missing a lot of detail here. How did you measure the components before variance\ndecomp? How was variance decomp done? What baseflow algorithm?\nWe now specify in the main text that we used variance decomposition with the Lindeman,\nMerenda, and Gold method to quantify contributions of runoff efficiency, precipitation\nefficiency, and baseflow conditions to residual streamflow variability. These components were\nderived from ERA5-Land and streamflow data, with baseflow estimated using the Lyne and\nHollick filter.\n• Fig 3: hard to distinguish colors\nWe have updated the figure colors to have greater contrast while being colorblind friendly.\n• Need a bit more of a transition between Fig 3b and 3c. How did you measure these? Where did\nthe data come from? Why these three factors and not others?\nTo better understand why runoff efficiency varies, we examined antecedent soil moisture,\nsnowmelt, and precipitation phase because these are three physically meaningful and\nindependent variables that showed the strongest associations with runoff generation among the\noptions considered. Values of these variables were derived from ERA5-Land reanalysis. These\ndetails have been added to the main text. In addition, as part of our response to Reviewer 2, we\nmore explicitly separate the two phases of analysis: (1) quantifying the importance of runoff\nefficiency, AR precipitation efficiency, and baseflow conditions, and (2) identifying the drivers\nof runoff efficiency. The results/discussion of 3b is now in a separate paragraph than those for\n3c.\n• Line 215: this information nicely answers my questions about why specifically ARs and runoff\nefficiency\nWe have added a reference to this in the introduction related to your earlier comment.\n• Line 235: Cite McKee et al. (1993) for SPI\nWe cite McKee et al. (1993) and have removed our second reference for clarity on which paper\nestablished and defined the metric\n• Can you provide a citation where SPI has been used as a proxy for ASM? Normally SPI is for\nmeteorological conditions. I also want more detail on how you calculated SPI: what distribution?\nHow did you do your seasonal adjustment? Or did you download it directly from USDM or\nsomething like that (strong argument for operational use)?\nThere is precedent in the literature for using SPI to represent soil moisture. Yuan et al. (2020)\ncompare multiple drought indices with in situ soil moisture observations in the U.S. Southern\nGreat Plains and find that SPI at monthly to seasonal timescales performs well in representing\nsoil moisture variability. Similarly, Cammalleri et al. (2024) compare global SPI datasets to\nmodeled soil moisture anomalies and find that one- to three-month aggregation periods best\ncapture soil moisture fluctuations with enough lead time to support early warning. Schoener and\nStone (2020) use an antecedent precipitation index (API), a metric similar to SPI, and show that\nit accurately predicts volumetric soil moisture at the catchment scale, with applications for flood\nforecasting in semi-arid basins.\nWe now refer to this variable as the Seasonal Standardized Precipitation Index (SSPI) to\ndistinguish it from traditional SPI. Like SPI, SSPI fits precipitation totals to a Gamma\ndistribution and transforms them into standardized values with a mean of 0 and a standard\ndeviation of 1 (McKee et al., 1993). The key difference is in the reference dataset: traditional 90-\nday SPI is calculated from precipitation totals for a fixed 90-day calendar period each year (e.g.,\nMay 1–July 31 across all years), whereas SSPI uses the full historical record of rolling 90-day\nprecipitation totals ending during cool-season months. This seasonal adjustment builds a larger\nreference distribution that captures both intra-seasonal and interannual variability, allowing\nprecipitation anomalies to be interpreted relative to the full range of wet-season conditions. For\neach AR event, SSPI is calculated from the 90 days of precipitation ending two days prior to\nlandfall. This approach parallels operational methods used by the U.S. Army Corps of Engineers\nAntecedent Precipitation Tool (APT) and USDA NRCS WETS tables, which benchmark rolling\nor cumulative precipitation totals against seasonally relevant distributions to provide\nhydrologically meaningful context for wetness or dryness assessments.\nThese additional details have been added to the main text of the manuscript and the\nSupplemental Methods:\n“There is precedent for using SPI as a proxy for soil moisture, with studies showing that SPI at\nmonthly to seasonal timescales closely tracks soil moisture anomalies” (Lines 254-256)\n“SSPI (calculated using the R package SEI) was selected as the antecedent moisture proxy due to\nits consistent performance across hydroclimatic settings and hydrologic interpretability. SSPI is a\nstandardized index following the SPI framework, but with a reference Gamma distribution fit to\nthe full historical record of rolling 90-day precipitation totals ending during the cool season,\npreserving seasonal context while supporting comparisons between years and regions. For each\nAR event, SSPI was calculated from precipitation over the 90 days ending two days prior to the\nstart of AR landfall. Additional details are provided in Supplementary Methods.” (Lines 37-543)\n• Line 250: that is a lot of additional work condensed into a few lines, well done\nWe are glad the presentation of this portion of the analysis was clear and concise.\n• Fig 4: really like this graphic, I think you distilled it nicely\nIt’s encouraging to hear that the figure successfully conveys the AR scale modification.\n• Could you make a confusion matrix? You’re kind of dancing around it, but you have hazardous\nvs. non-hazardous ARs and flood-causing vs. non-flood-causing ARs, and you’ve reported false\npositives and false negatives without using that terminology explicitly. It would be nice to\nsummarize it in a table, and I think it would be helpful to see those numbers reported before and\nafter your rank modifications.\nWe appreciate this helpful suggestion. We have added a confusion matrix summarizing the\nclassification performance of the original and SPI-modified AR scales in Figure 5a. This panel\nshows the numbers and proportions of correctly and incorrectly classified events (true positives,\nfalse positives, true negatives, and false negatives) for both California and central Chile.\n• The “greater than 50% of adjacent AR rank pairs with statistically significant differences in\nQmax” is a super clunky metric. You’ve described it well, but I just have no sense of what it\nmeans in a practical sense. What exactly is it you’re trying to convey to the reader with this?\nCould you give it your own name to make that more clear, maybe “ability to differentiate\nranks” or “rank organization metric” or something like that?\nWe revised this section to describe the metric as the separation of flood response by AR rank,\nwhich we now define as the median percentage of adjacent AR rank pairs with statistically\ndifferent peak streamflow distributions across catchments. This replaces the earlier framing\nbased on the proportion of sites exceeding a 50% threshold. Thank you for this suggestion. We\nfeel that the updated metric more clearly conveys a metric characterizing the differences in\nexpected flood response between AR scale ranks.\n• Fig 5a: this was a stacked boxplot in Fig 1a. Could you repeat that here for visual consistency?\nIt wasn’t clear to me at first that these add up to 100%\nWe have remade this figure to have stacked boxplots for visual consistency with Figure 1.\n• Line 298: where did the 2degC threshold come from? Literature or your own analysis?\nThe 2 °C threshold is based on our own analysis, derived from Figure 6a. We have revised the\ntext to remove the explicit threshold and instead describe the broader pattern observed colder\ncatchments show smaller improvements in correlation under the SPI-modified AR scale.\n• Can you add the p_Qmax plots from Fig S5 to Fig 2? Similarly, can you add the p_Qmax(new)\nplots to Fig 6 along with the difference plots?\nWe have added the Spearman correlation plots originally in the SI to Figures 2 and 6.\n• Fig 7: love the case study. I have an issue with the SPI though. If it’s a normal distribution (and\nyou would expect it to be) an SPI of +/-5 would have a return period on the order of once every\n3.5 million years. I know 2017 was a notable water year, but I‘m suspicious that it’s THAT\nnotable, and to go from -5 to +5 over the course of a few months seems highly unlikely\nThank you for pointing this out—this was a really helpful catch. We investigated the issue and\nrealized there was a small error in the way we were fitting the gamma distribution for SPI. A\ntypo in our code caused the SPI values to have much longer tails than they should have—roughly\n2x too long. We’ve corrected this, and the resulting distribution of SPI values is now much more\nin line with probability values we would expect (see updated SPI value histogram in SI Figure\n5b). The full range is now approximately ±2.5, with a maximum value of 3, equivalent to a\nreturn interval of ~750 years. The revised SPI time series still shows the same wet/dry pattern\nduring the case study, just with more realistic magnitudes. This correction didn’t change any of\nour main findings. The only adjustment is that the optimal SPI threshold for modifying the AR\nscale is now ±0.5 instead of ±1.0. That revised threshold corresponds to a return interval of ~3\nyears.\n• Line 393: CAMELS typo\nAmended in text, thank you.\n• Line 397: Why the >250km from the coast restriction?\nThis is a typo and should be <250 km from the coast, but this is just the farthest distance\nwatersheds in central Chile and California exist from the coast. However, this isn’t an essential\nselection criterion, and as such, we have removed it from the text for concision.\n• Line 398: how was spatial independence assessed?\nSpatial independence was determined based on choosing non-overlapping or nested watersheds.\nWe have amended the text to better reflect this: “spatial independence (i.e., non-nested and non-\noverlapping).”\n• Line 418: are ARs assumed to move only horizontally as they move inland?\nNo, we don’t assume ARs move purely zonally. Many ARs have a meridional or tilted\norientation, and their precipitation footprint can extend well north or south of the landfall point.\nTo account for this, we used a ±2° latitude window around each catchment to assign AR\nlandfalls from nearby coastal grid cells. This helps capture the broader latitudinal influence of\nARs, especially for events that don’t make landfall directly west of the watershed.\n• Line 428: I like the AR_continuous, very cool and useful idea. Which statistical analyses is it\nused for? It’s not clear in the results which tests are run on this vs. the actual ranks\nAR_continuous, referred to in the results/discussion as the “interpolated AR scale” is used in the\nSpearman rank correlations with precipitation and streamflow and to fit the GLM models\npredicting flood response. We have added a sentence to the methods (line 507-510) and\nassociated figure captions to explicitly state what statistical tests this variable is used in.\n• Line 442: why do this splitting?\nWe split longer precipitation events when separated by a local minimum because we observed\nthat distinct streamflow peaks were often driven by different ARs within the same multi-day\nprecipitation period. This approach allows for more accurate pairing between precipitation\nstreamflow events with independent ARs.\n• Line 448: little more detail on the return interval method?\nThe return interval of peak streamflow was estimated following the U.S. federal guidelines for\nflood frequency analysis (Bulletin 17C; England et al., 2019), using the Log-Pearson Type III\ndistribution and the USGS PeakFQ software. We applied this method to daily peak streamflows\nat each site, as detailed in Webb et al. (2025). However, we are now defining flood events as\nthose with a normalized peak streamflow ≥ 1, indicating a flood event equal to or larger than the\ncatchment’s mean annual flood.\n• Line 489: Spearman correlations between ASM/phase ratio/snowmelt and… is the Y variable\nthe Qmax residuals? Unclear here\nThese Spearman correlations were between runoff efficiency and the hydrometeorological\nexplanations (ASM, phase ratio, snowmelt). We have modified the text to state this. Line 526-\n529: “Because runoff efficiency, primarily driven by antecedent soil moisture, emerged as the\ndominant source of residual peak streamflow variability not captured by AR scale rank, we\nexplored how proxy variables for antecedent moisture could be incorporated into the AR scale’s\nstructure to improve its characterization of flood hazards”\n• Line 508: explain “non-overlapping 90-day intervals.” Is this your seasonal adjustment? What are the start and end dates of\neach interval?\nWe have revised the text to clarify the meaning of “non-overlapping 90-day intervals” and\nreplaced the phrase “seasonally-adjusted SPI” with the more accurate term “SPI based on cool\nseason climatology” to avoid confusion. Specifically, we calculated SPI for each AR event based\non the total precipitation accumulated during the 90-day period ending two days prior to each\nevent’s AR landfall. These values were then standardized using a gamma distribution fit to the\nfull record of rolling 90-day precipitation totals during the cool season. These 90-day windows\nwere defined by dividing the daily precipitation time series into rolling blocks, beginning with\nthe first available day in the record. For example, if the record begins on October 1, 1950, the\nfirst interval spans October 1 to December 29, the second from December 30 to March 29, and\nso on. Only 90-day blocks ending in the cool-season months of the respective region were used\nto fit the gamma distribution. This approach retains the signal of natural seasonal variability in\nprecipitation by referencing the full annual cycle, rather than stratifying by season or calendar\nmonth. We have also added a citation for the R package (SEI) used to compute SPI values.\n• I would love a summary table in text to compare the statistical test results of the original vs.\nmodified scale.\nWe have added Table 1 to the manuscript to summarize the statistical test results as requested.\n• The data is available, but the code is not. This is not a reproducible product. Please publish, at a\nminimum, scripts that recreate the main manuscript figures, and ideally the full R workflow.\nAdditionally, please add citations for key packages used in this analysis.\nWe have made both the data and code available at\nhttp://www.hydroshare.org/resource/cee30237618d48e8bb0d09ecff9a4a7c. The repository\nincludes all scripts needed to reproduce the statistical analysis and main manuscript figures. In\naddition, for the resubmission, we have included Supplementary Data files containing all\nstatistical results used in the paper as part of the Supplementary Information. We have also added\ncitations in the methods for key R packages used in the analysis, as requested.\nReviewer #1 (Remarks on code availability):\nThe data is available, but the code is not. This is not a reproducible product. Please publish, at a\nminimum, scripts that recreate the main manuscript figures, and ideally the full R workflow.\nSee above comment regarding the code availability."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for their detailed responses. I have no further comments. I think this is really high-quality work, and I look forward to seeing it in print.", "reviewer_2": "The authors have responded very thoroughly to my comments and those from the other reviewers.", "reviewer_3": "I have received the revised manuscript by Webb et al. and I would like to commend the authors on conducing excellent and comprehensive revisions! All of my major concern points are clarified and addressed. I think the manuscript is now in an excellent shape and will be a valuable contribution for the Nature Communication readership. I have a very few very minor suggestions that could be further helpful to the authors.\nSpecific comments to the Main\nLine 97: I would suggest to provide here the length of records explicitly.\nLine 100: Typically, the term is “mean maximum annual flood”. Please also refer to my later comment to the Methods – a clear terminology has to be used to avoid ambiguity in definitions.\nFigure 1: Please explain the procedure for the normalization in the caption to clarify the meaning of the y axis label in panel b. I would also suggest to add a horizontal line at y=1 for panel p to highlight the events that are further considered as floods in the manuscript and add their number to explicitly show the contrast with the total number of streamflow events below the flood threshold.\nSpecific comments to Methods\nLine 471-473: It is not quite clear why the authors have opted to use the mean maximum flood of cool season for normalization of streamflow events and not a more standard mean maximum annual flood. This needs to be clarified.\nMoreover, if the authors do opt for the seasonal maxima instead of the annual one, this has to be clearly reflected in the term they use in the manuscript (i.e., seasonal maxima instead of annual maxima) to avoid any possible confusion. Cool season period needs also to be explicitly defined."}, "author_response": "Thank you to the authors for their detailed responses. I have no further comments. I think this is really high-quality work, and I look forward to seeing it in print.\nThank you for your efforts in reviewing. We appreciate it.\nReviewer #1 (Remarks on code availability):\nCode repo now looks complete. The readme is well done, I didn't try to open or run any files but\nthe description is sufficient to do so.\nWe are glad that you approve of our code and readme files.\nReviewer #2 (Remarks to the Author):\nThe authors have responded very thoroughly to my comments and those from the other\nreviewers.\nWe are glad that our response meets your standards and are grateful for the time you put in to\nreview our work.\nReviewer #3 (Remarks to the Author):\nI have received the revised manuscript by Webb et al. and I would like to commend the authors\non conducing excellent and comprehensive revisions! All of my major concern points are\nclarified and addressed. I think the manuscript is now in an excellent shape and will be a\nvaluable contribution for the Nature Communication readership. I have a very few very minor\nsuggestions that could be further helpful to the authors.\nWe are pleased that you think our previous revisions resolved your previous concerns. Thank\ny\nThank you again for your detailed review. We have responded to each of your comments below.\nSpecific comments to the Main\nLine 97: I would suggest to provide here the length of records explicitly.\nWe have added details about the streamflow record length requirement, which is at least 20 years\nof records between 1950-2023. “Next, we quantify AR event flood response using the event peak\ndaily streamflow occurring during or up to two days after the AR precipitation event from long-\nterm gaging stations within ±2° latitude (approximately half the width of the median AR43) of\nthe landfalling AR and with at least 20 years of records between 1950-2023.”\nLine 100: Typically, the term is “mean maximum annual flood”. Please also refer to my later\ncomment to the Methods – a clear terminology has to be used to avoid ambiguity in definitions.\nThank you for this comment. To be more precise in our terminology, we have replaced the\nacronym MAF (maximum annual flood) with mean seasonal maximum flood. On lines 125-126,\nwe clarify that the mean seasonal flood magnitude is for the cool season months, defined as\nOctober through April in California and April through October in central Chile. We have\nupdated the terminology throughout the rest of the paper to use ‘seasonal maximum floods’.\nFigure 1: Please explain the procedure for the normalization in the caption to clarify the meaning\nof the y axis label in panel b. I would also suggest to add a horizontal line at y=1 for panel p to\nhighlight the events that are further considered as floods in the manuscript and add their number\nto explicitly show the contrast with the total number of streamflow events below the flood\nthreshold.\nWe have added details to the figure caption that the peak streamflow value has been normalized\nby the mean seasonal maximum flood. We have also added these details to the Figure 5 caption.\nIn panel b for both Figure 1 and Figure 5 we have added a clearer demarcation of which events\nare considered floods and the number of floods.\nSpecific comments to Methods\nLine 471-473: It is not quite clear why the authors have opted to use the mean maximum flood of\ncool season for normalization of streamflow events and not a more standard mean maximum\nannual flood. This needs to be clarified. Moreover, if the authors do opt for the seasonal maxima\ninstead of the annual one, this has to be clearly reflected in the term they use in the manuscript\n(i.e., seasonal maxima instead of annual maxima) to avoid any possible confusion. Cool season\nperiod needs also to be explicitly defined.\nWe are using the mean maximum flood of the cool season because atmospheric river floods\nprimarily occur in the cool season. A number of our sites are snow-dominated and receive\nsignificant flows during the melt season, hence, some of the maximum annual floods are melt-\ndriven. In the literature on atmospheric river hydrology, it is common practice to limit the\nstreamflow considered to the cold season (e.g., Dettinger 2011 and Albano et al. 2020).\nFurthermore, we used seasonal instead of annual maxima to avoid drawing from a mixed\npopulation of floods. Previous work has shown that a series of annual maximum floods likely is\nmade up of floods generated by distinct processes (e.g., Barth et al. 2017). This allows us to\nnormalize the magnitude of AR-driven streamflow by the mean maximum of AR-driven floods.\nWe have added justification to the methods on lines 404-408. Additionally, please see our above\ncomment regarding the change in terminology from mean maximum annual flood to mean\nseasonal maximum flood to avoid confusion. We have added a definition for the cool season\nearlier in the manuscript on line 119."}]

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