Abstract

Precipitation extremes are projected to become more frequent along the U.S. West Coast due to increased atmospheric river (AR) activity, but the frequency of less intense precipitation events may decrease. Antecedent soil moisture (ASM) conditions can have a large impact on flood responses, especially if prestorm precipitation decreases. Taken together with increased antecedent evaporative demand due to warming, this would result in reduced soil moisture at the onset of extreme precipitation events. We examine the impact of ASM on AR-related floods in a warming climate in three basins that form a transect along the U.S. Pacific Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We ran the Distributed Hydrology Soil Vegetation Model (DHSVM) over the three river basins using forcings downscaled from 10 global climate models (GCMs). We examined the dynamic role of ASM by comparing the changes in the largest 50, 100, and 150 extreme events in two periods, 1951–2000 and 2050–99. In the Chehalis basin, the projected fraction of AR-related extreme discharge events slightly decreases. In the Russian basin, this fraction increases, however, and more substantially so in the Santa Margarita basin. This is due to increases in AR-related extreme precipitation events, as well as the fact that the relationship of extreme precipitation to extreme discharge is strengthened by projected increases in year-to-year volatility of annual precipitation in California, which increases the likelihood of concurrent occurrence of large storms and wet ASM conditions.

1. Introduction

Atmospheric rivers (ARs) are responsible for most floods and flood damages along the U.S. West Coast (e.g., Ralph et al. 2006; Dettinger et al. 2011; Neiman et al. 2011; Barth et al. 2017; Konrad and Dettinger 2017; Corringham et al. 2019). Over the past decade, several studies have examined the potential impact of climate change on AR landfalling activity in this region, in order to better project the changes in extreme precipitation associated with ARs. Using seven global climate models (GCMs), Dettinger (2011) projected that the number of landfalling ARs in California would increase by ~30% in the twenty-first century. The peak AR intensity, storm temperature, and the length of AR season would increase as well (Dettinger 2011). Espinoza et al. (2018) cross compared their results with previous studies focused on the U.S. West Coast (Hagos et al. 2016; Gao et al. 2015; Payne and Magnusdottir 2015; Warner et al. 2015; Shields and Kiehl 2016). The projected changes in landfalling AR frequency ranged from +8% to +230%–290% among these studies under the representative concentration pathway (RCP) 8.5 emissions scenario. Despite great discrepancies in the projections, all of these studies showed that the number of landfalling AR days will substantially increase by the end of twenty-first century along the U.S. West Coast, as will the AR intensity [the integrated water vapor transport (IVT)] (Warner et al. 2015; Espinoza et al. 2018).

The projected increases of the AR frequency and intensity will increase both the number of extreme precipitation days and the peak precipitation intensity along the U.S. West Coast where they interact with complex topography and lead to orographic precipitation (Hagos et al. 2016; Warner et al. 2015; Gershunov et al. 2019). Payne et al. (2020) reviewed studies on responses and impacts of ARs to climate change. They also showed that precipitation extremes associated with ARs will increase over the western U.S. in a warming climate, which will affect flood risk.

A commensurate increase in high flows with respect to the increase in precipitation extremes, however, may not be expected on the basis of theory and observational evidence (Sharma et al. 2018). Antecedent soil moisture (ASM) conditions play an important role in the linkage between extreme precipitation and flooding. To examine this relationship, Ivancic and Shaw (2015) used daily precipitation and streamflow records, and monthly soil moisture from model simulations during 1950–2000 in 390 unregulated watersheds over the contiguous United States. They found that aggregated across all sites, extreme (99th percentile) precipitation only led to extreme (99th percentile) discharge 36% of the time. However, the percentage increased to 62% during the periods when antecedent soil moisture was wet. Bennett et al. (2018) examined the effect of ASM (using antecedent precipitation as a proxy) on flood volume in 100 Australian catchments with subdaily streamflow and precipitation observations. They found that although flood-producing precipitation was the dominant driver of flood magnitudes, the elasticity of flow to antecedent precipitation was about one-third of the elasticity to flood-producing precipitation; however, the influence of antecedent precipitation weakened as event magnitudes increased. Furthermore, Wasko and Sharma (2017) examined the sensitivity of extreme daily precipitation and discharge to changes in daily temperature based on station records. They found that changes in heavy rainfall events linked with observed warming did not lead to similar changes in streamflow in most regions globally possibly due to initial moisture conditions.

Changes in ASM and their impact on flood response are especially important in areas like the U.S. West Coast where flooding is typically caused by large single precipitation excess events (Berghuijs et al. 2016) and where precipitation is strongly winter dominant, with many potentially flood-inducing events occurring relatively early in the wet season when soils tend to be dry. Some studies have examined the impact of ASM on AR-related flooding in the region. For example, Leung and Qian (2009) used Weather Research and Forecasting (WRF) Model simulations to examine AR-induced heavy precipitation and flooding events over the western United States during 1980–2000. They found that for two selected events with similar amounts of total precipitation, different ASM conditions could lead to a difference of more than 0.3 in the runoff ratio. Cao et al. (2019) examined the role of ASM in historical AR-related flooding on California’s Russian River basin, a coastal watershed whose winter precipitation extremes are dominated by ARs. They showed that low ASM was an offsetting factor for the three AR category 5 [the highest category of ARs on the scale defined by Ralph et al. (2019)] events that did not lead to major flooding during 1980–2017.

Several studies suggest that although the frequency of extreme precipitation will increase, the frequency of low- to medium-intensity precipitation will decrease and offset the change in annual precipitation along the U.S. West Coast (e.g., Pierce et al. 2013b; Polade et al. 2017; Gershunov et al. 2019). By using 16 GCMs from the phase 5 of Coupled Model Intercomparison Project (CMIP5), Gershunov et al. (2019) examined the role of ARs in future precipitation regime change over the western United States. They identified five GCMs (denoted as Real-5) that they deemed most realistic in terms of their performance in capturing the statistics of historical AR events and AR contribution to total annual precipitation in comparison with an AR catalog (denoted as SIO-R1; Gershunov et al. 2017) derived from the NCEP–NCAR reanalysis dataset. Along the U.S. West Coast, they found (based on the Real-5) that the frequency of the heavy (90th–99th percentile) and extreme (>99th percentile) precipitation generally increased due to increased AR activity, with the greatest increase projected in Northern California, while the frequency of medium-intensity (30th–90th percentile) precipitation decreased due to non-AR events. These changes in low- to medium-intensity precipitation would lead to lengthening dry periods (punctuated by stronger precipitation extremes); taken together with increased temperature, they may exert a synergic effect on ASM, thus mitigating flood response to increased extreme precipitation.

Moreover, the role of ARs in future precipitation regime change is different in the Pacific Northwest and California due to differences in their climatologies. Gershunov et al. (2019) found that the Real-5 GCMs projected, with a significant reduction of projection uncertainty in comparison with the full ensemble, less frequent but more intense precipitation from ARs. This in turn translated into more volatility of year-to-year total annual precipitation, especially in California. This suggests a more flood- and drought-prone future precipitation regime and may affect the importance of ASM considerations.

Given this background, we address here the following motivating questions:

  1. How will ARs’ contribution to floods change along the U.S. West Coast by the end of twenty-first century?

  2. How will climate change affect ASM and what is its role in modulating flood response in a warming climate?

2. Study region

We selected three watersheds that form a transect along the U.S. Pacific Coast: the Chehalis River basin in Washington State, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. The three river basins have drainage areas of 5400, 3850, and 1870 km2, respectively (see Fig. 1). We selected these three basins as our study domain because 1) they are coastal watersheds frequented by ARs and their geographical locations reflect different AR landfalling signatures (e.g., Gershunov et al. (2019) used the first two and another one next to the Santa Margarita River basin to represent projected precipitation regimes in the western U.S. coastal domain); 2) all of them are rain-dominant basins with relatively modest topographic variations (elevation ranges of 0–1429, 0–1324, and 143–1736 m, respectively; only the Chehalis has modest contributions of snowmelt to flood runoff), which avoids the added complexity of the influence of snowmelt on streamflow that more commonly occurs in mountainous basins; 3) they are somewhat less developed (although there are two dams in the Russian River basin) in comparison with surrounding heavily developed and urbanized basins.

Fig. 1.

Map of study region including (a) the Chehalis River basin in Washington State, (b) the Russian River basin in Northern California, and (c) the Santa Margarita River basin in Southern California. The Kling–Gupta efficiency (KGE) during the calibration period (1986–2000) is displayed for each stream gauge in (a)–(c). (d) Location of 60 unregulated coastal watersheds and their hydroclimatic conditions, including (e) annual precipitation, (f) seasonality of precipitation, and (g) max flood ratio. The locally weighted scatterplot smoothing (LOWESS) curve is displayed in (e)–(g).

Fig. 1.

Map of study region including (a) the Chehalis River basin in Washington State, (b) the Russian River basin in Northern California, and (c) the Santa Margarita River basin in Southern California. The Kling–Gupta efficiency (KGE) during the calibration period (1986–2000) is displayed for each stream gauge in (a)–(c). (d) Location of 60 unregulated coastal watersheds and their hydroclimatic conditions, including (e) annual precipitation, (f) seasonality of precipitation, and (g) max flood ratio. The locally weighted scatterplot smoothing (LOWESS) curve is displayed in (e)–(g).

The Pacific Coastal region has strongly winter-dominant precipitation and mostly dry summers. The annual precipitation of the Chehalis, Russian, and Santa Margarita basins ranged between 1500 and 3100 mm, between 500 and 2200 mm, and between 145 and 905 mm, respectively, during water years (WY) 1951–2000, with 85%, 95%, and 91% of precipitation falling between October and April.

3. Data and methods

We implemented a parallel version (Perkins et al. 2019) of the Distributed Hydrology Soil Vegetation Model (DHSVM) (Wigmosta et al. 1994) in the three basins. We ran it at an hourly time step in order to obtain full hydrographs of storm events from subdaily data and soil moisture conditions. Our analyses include two parts: 1) we examine the AR contribution to historical floods and the role of ASM on historical AR flooding in each of the basins during WY 1951–2000 using meteorological forcings from gridded observations; 2) we assess the changes in AR contribution to future flood events and the changes in ASM using downscaled GCM forcings (under the RCP8.5 scenario) by comparing simulated flood statistics for the periods WY 1951–2000 with WY 2050–99.

a. Meteorological forcing data

1) Historical events

For historical events, we used the meteorological forcings of Livneh et al. (2015) for the period of WY 1951–2000. It is a daily dataset of gridded observations at a spatial resolution of 1/16°, with variables including daily precipitation and daily maximum and minimum temperature. Its wind data are from the lowest level of the National Centers for Environmental Prediction and National Centers for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al. 1996). DHSVM requires meteorological inputs including precipitation, wind speed, air temperature, relative humidity, downward solar and longwave radiation at the model’s hourly time step. The hourly calculation of the last four variables was performed using the Mountain Microclimate Simulation Model (MTCLIM) algorithms as described and implemented by Bohn et al. (2013). Wind speed was taken to be constant throughout a day. Given that fine timescale precipitation data are important to hydrologic predictions in small watersheds, we separately describe the hourly disaggregation of precipitation in section 3a(3).

2) GCM projected changes

We used downscaled forcings from 10 GCMs (see Table S1 in the online supplemental material) under the RCP8.5 emissions scenario for the control period of WY 1951–2000 and the future period of WY 2050–99. We considered only RCP8.5 as did Gershunov et al. (2019), from which we obtained the AR catalogs. Pierce et al. (2014) statistically downscaled the CMIP5 GCM daily minimum and maximum temperature, and daily precipitation to 1/16° using localized constructed analogs (LOCA), with Livneh et al. (2015) dataset as the observed training dataset. The disaggregation of daily to hourly meteorological inputs was the same as for historical events.

The basis of our selection of GCMs is studies like Gershunov et al. (2019) and Rupp et al. (2013) that have evaluated GCM performance in specific regions, and screened out GCMs that perform poorly relative to observations in an historical period. As mentioned above, Gershunov et al. (2019) evaluated the performance of 16 GCMs that archived variables sufficient to identify ARs in reproducing the key statistical features of historical landfalling AR activity along the U.S. West Coast. We selected 10 GCMs for this study based on their evaluation results, including their Real-5 (see Table S1). The larger set of 10 GCMs, among the 16 GCMs used in Gershunov et al. (2019), also showed relatively good credibility in reproducing observed metrics of precipitation at the seasonal, annual, and decadal scales in the Southwest (Rupp et al. 2013).

3) Hourly disaggregation of precipitation

Following Westra et al. (2012), we disaggregated the gridded daily precipitation in each basin to hourly using a regionalized method of fragments (denoted as MoF) algorithm. We first collected hourly precipitation data from NOAA’s Hourly Precipitation Data (HPD) database. We selected stations within a buffer of 15 km from each basin boundary and with records longer than 5 years. There are 12, 25, and 15 stations that met our criteria in the Chehalis, Russian, and Santa Margarita basins, respectively (see gauge locations in Fig. 1). Some stations have records that go back at least to WY 1951. We performed a simple quality control for the hourly stations following Cao et al. (2018), where we compared each station with neighboring stations to screen for outliers, such as improbable zero values and unusually high values.

Based on the MoF method, for a given 1/16° grid cell and for a given wet day, we searched for days within a moving window of ±15 days centered on that day across all years of record and across four nearby HPD stations, from which we selected the wet days from station data with the same previous- and next-day wetness state (i.e., precipitating or not) as the grid data to account for continuity. The selected daily station precipitation was then ranked by its absolute deviation from the gridded daily precipitation. We found up to 10 nearest neighbors with absolute deviations less than 10% of the gridded precipitation, and randomly drew one of them with probabilities determined by the previous ranking. The selected fragment (hourly ratio of station data to its daily precipitation) was then multiplied to the grid daily precipitation. If there were no match found, the daily precipitation would be apportioned evenly.

b. Model implementation

We implemented the DHSVM at a spatial resolution of 150 m over the three basins. The model inputs include DEM, soil class, land cover type, soil depth and flow direction. The first three were taken from the NASA Shuttle Radar Topography Mission (SRTM) 90-m product, the USDA STATSGO2 data, and the U.S. Geological Survey (USGS) 30-m GAP/LANDFIRE land cover map based on the 2001 imagery, respectively, which were resampled to model’s 150-m resolution. The latter two were determined using scripts included in DHSVM, in which soil depths were estimated based on local slope, upstream source area, and elevations. In this study, we did not consider future land cover change.

We first calibrated the model in each basin using meteorological forcings from gridded observations, for which we selected stream gauges with valid daily records for at least 85% of the period from 1950 to 2000 throughout each basin. This resulted in sets of six, six, and three gauges in the Chehalis, Russian, and Santa Margarita basins, respectively (see Fig. 1 for stream gauge locations). We then ran the model and evaluated simulations of historical flood events and GCM-projected changes.

c. AR-related extreme events

We first used the peaks over threshold (POT) method to identify extreme precipitation and extreme discharge events. For the latter, we also examined the annual maximum flow (AMF) events as this is a common characterization of flood flows. We then examined ARs’ contribution to extreme events by identifying the ones that were coincident with AR events.

We selected POT extreme precipitation and extreme discharge events based on daily data. The POT method samples observations above a given threshold value and considers a wider range of events than the block maxima approach (e.g., Lang et al. 1999; Beguería et al. 2011; Mallakpour and Villarini 2017). We first applied independence criteria to the data. We defined precipitation events based on consecutive days with peak daily precipitation exceeding a given threshold. The precipitation events were separated from each other by at least one day with daily precipitation below a certain POT threshold. We selected thresholds to result in 1, 2, and 3 events per year on average, which we denote as POTN1P, POTN2P, and POTN3P. Because we are interested in the dynamic role of ASM on floods in a changing climate, we used separate thresholds for the historical climate and future climate periods, which results in same numbers of events for the two periods and keeps a focus on the largest ones.

For extreme discharge, we used the independence criteria from the U.S. Water Resources Council (USWRC 1982) for daily streamflow. The second flood peak of two consecutive events must be rejected if

 
Ninterval<5days+log(A)orXmin<(3/4)min[Q1,Q2],
(1)

where Ninterval is the number of interval days between two peaks, A is the basin area in square miles, Xmin is the minimum intermediate flow between two peaks, and Q1 and Q2 are two consecutive peak values. Similar to precipitation events, we set thresholds (separately for the two climate periods) for daily streamflow at each stream gauge to result 1, 2, and 3 extreme discharge events per year on average, which we denote as POTN1D, POTN2D, and POTN3D.

To examine the AR contribution to extreme events, we identified the POT events as well as AMF that were coincident with AR events. For historical events, we used the reanalysis-based SIO-R1 AR catalog (Gershunov et al. 2017). For the GCM projected changes, we used the AR catalogs of Gershunov et al. (2019), which they developed through application of the same automated AR detection scheme that they applied to historical observations to daily GCM output. For each catalog, we extracted the grid cells that intersected each basin and identified landfalling ARs.

d. The role of ASM

We examined the role of ASM on historical floods by evaluating the relationship between precursor soil moisture and storm runoff–precipitation ratios, which we took as storm total runoff volume divided by storm total precipitation. We used the definition of precipitation and runoff events following Cao et al. (2019). For a POT extreme precipitation event selected based on daily data, we calculated the storm total precipitation based on hourly data. We took the beginning of a precipitation event as the first hour with precipitation exceeding a certain threshold and the end of event as the hour with precipitation dropping below that threshold. The summation of precipitation over the event hours is the storm total precipitation. For a POT extreme runoff event selected based on daily data, we calculated the storm total runoff using hourly model simulations. The start of a runoff event is the hour of the rise of the hydrograph and the end of event is determined by the constant-k method of Blume et al. (2007) but no longer than three days after the peak hour or the start of the following event. We defined ASM as the minimum value of the hourly surface-layer soil moisture from model simulations within the 24 h prior to the start of a precipitation event.

We examined the role of ASM in modulating flood response in a warming climate by assessing the changes in the connection between extreme precipitation and extreme discharge events during two periods, WY 1951–2000 and WY 2050–99. Specifically, we examined changes in the probability of extreme precipitation events leading to extreme discharge events of the same POT threshold, which we denote as Pr(POTN1D|POTN1P), Pr(POTN2D|POTN2P), and Pr(POTN3D|POTN3P) for events with a threshold set to 1, 2, and 3 events per year on average. Using the Pr(POTN1D|POTN1P) for example, for each GCM and for each time period, the probability was calculated as the fraction of POTN1D events occurred following POTN1P events. For each POTN1P extreme precipitation event, we could identify its corresponding discharge event and check whether the peak flow exceeds the threshold of POTN1D. We then had two groups of probabilities from 10 GCMs for the two time periods. Finally, we examined whether there is any significant changes between these two groups of probabilities using the Wilcoxon signed-rank (nonparametric) test following Maurer et al. (2018).

4. Results

a. Model evaluation

We used the Kling–Gupta efficiency (KGE) (Gupta et al. 2009), normalized root-mean-square error (NRMSE) and relative bias to evaluate the goodness-of-fit between daily streamflow observations and aggregated daily simulations at each gauge (see Table S2 and Fig. 1). In the Chehalis basin, KGE, NRMSE, and the relative bias are 0.69–0.89, 0.36–0.49, and from −10% to 23%, respectively, across the six gauges during the calibration period (1986–2000). In the Russian basin, the downstream gauges are influenced by two reservoirs. We obtained the naturalized flows at these gauges by calculating the difference between simulated streamflow without and with implementation of the DHSVM reservoir module (Zhao et al. 2016) at each gauge and then adding the difference back to the observations, following Cao et al. (2019). After doing so, KGE, NRMSE, and the relative bias are 0.68–0.93, 0.24–0.46, and from −6% to 28%, respectively, across the six gauges. KGE is highest at the downstream-most gauge. In the Santa Margarita basin, KGE, NRMSE, and the relative bias are 0.53–0.74, 0.66–0.68, and from −32% to 3%, respectively, across three gauges. The NRMSE is larger in this basin compared with the other two partly due to its smaller magnitude of streamflow. Model performance in the verification period (1971–85) is similar to that during the calibration period.

We also evaluated model performance for peak flows. Figure 2 shows the simulated peak (daily) discharge in POTN3D in comparison with observations during the period of WY 1951–2000 at selected upstream and downstream USGS gauges. The simulated peaks show reasonable matches with observed peaks, with KGE values of 0.71, 0.69, and 0.73 at upstream gauges in the Chehalis, Russian, and Santa Margarita River basins, respectively, and with KGE values of 0.75, 0.68, and 0.66 at downstream gauges in three basins, respectively.

Fig. 2.

Comparison of simulated and observed POTN3D (extreme discharge events with threshold set to three events per year) at selected (a)–(c) upstream and (d)–(f) downstream USGS gauges in three basins during the period WY 1951–2000.

Fig. 2.

Comparison of simulated and observed POTN3D (extreme discharge events with threshold set to three events per year) at selected (a)–(c) upstream and (d)–(f) downstream USGS gauges in three basins during the period WY 1951–2000.

b. Historical events

1) Role of ARs

We set thresholds of POT extreme events during WY 1951–2000 to make sure there were 1, 2, and 3 events per year on average. For extreme precipitation, 60%–74% (depending on the POT threshold used and increasing as the threshold increases) of the POT events were coincident with ARs in the Chehalis River basin, based on the SIO-R1 AR date catalog (see Table S3). In the Russian and Santa Margarita River basins, the AR-related percentages were 95%–98% and 60%–78%, respectively. The percentages were highest in the Russian River basin, which is located in the vicinity of the most intense AR activity along the U.S. West Coast. In particular, ARs have the greatest contribution to total annual precipitation along the U.S. West Coast in this area (Gershunov et al. 2017). The AR-related percentages were lower for the model historical simulations based on the ensemble mean of all 10 GCMs, 51%–60%, 65%–75%, and 31%–41%, respectively, in three basins (see Table 1). The AR-related percentages based on the mean of Real-5 GCMs matched with the observations slightly better than the full ensemble except in the Santa Margarita basin (see Table S3).

Table 1.

Fraction (%) of AR-related POT extreme precipitation events, POT extreme discharge events, and AMF events in three river basins based on the ensemble average of 10 GCMs.

Fraction (%) of AR-related POT extreme precipitation events, POT extreme discharge events, and AMF events in three river basins based on the ensemble average of 10 GCMs.
Fraction (%) of AR-related POT extreme precipitation events, POT extreme discharge events, and AMF events in three river basins based on the ensemble average of 10 GCMs.

For extreme discharge, 66%–80%, 85%–98%, and 51%–72% (70%, 86%, and 60%) of the POT events (AMF events) were coincident with ARs in three basins, respectively, based on the SIO-R1 AR date catalog (see Table S4). Figure 3 shows the POTN3D events that were coincident with ARs in three basins based on this catalog. The AR contribution to extreme discharge also increased as the POT threshold increased. The AR-related percentages of POT events (AMF events) were lower based on the ensemble mean of all 10 GCMs, which were 47%–58%, 48%–67%, and 25%–34% (51%, 59%, and 30%), respectively, in three basins (see Table 1). Similar to extreme precipitation events, the AR-related percentages based on the mean of the Real-5 GCMs matched the observations slightly better than the full ensemble except in the Santa Margarita basin (see Table S4). In summary, the GCM-based results capture the geographic pattern of historical relative contributions of ARs to extreme precipitation and discharge in the three basins, although the models tend to underestimate the magnitudes of these contributions.

Fig. 3.

AR-related POTN3D (extreme discharge events with threshold set to three events per year on average) based on simulated daily streamflow at basin outlets of (a) Chehalis River basin, (b) Russian River basin, and (c) Santa Margarita River basin during WY 1951–2000.

Fig. 3.

AR-related POTN3D (extreme discharge events with threshold set to three events per year on average) based on simulated daily streamflow at basin outlets of (a) Chehalis River basin, (b) Russian River basin, and (c) Santa Margarita River basin during WY 1951–2000.

2) Role of ASM

We examined the relationship between the ASM and runoff ratio in the POTN3P events. Our previous analysis in the Russian basin (Cao et al. 2019) showed that the runoff ratio was much more strongly related to ASM than to storm precipitation. The same was found in the other two basins (see Fig. 4). Following Crow et al. (2017), we used the Spearman’s rank correlation coefficient Rs to evaluate the strength of the potentially nonlinear relationship between ASM and the runoff ratio, which were 0.76, 0.81, and 0.74 in the three basins, respectively.

Fig. 4.

Boxplots with an interval of 20th percentile of the simulation-based runoff ratio vs storm total precipitation and ASM of POTN3P (extreme precipitation events with threshold set to 3 events per year on average) in three basins during WY 1951–2000. The tick labels of x axis show the 0–20th, 20th–40th, 40th–60th, 60th–80th, and 80th–100th percentiles of either storm precipitation or ASM values.

Fig. 4.

Boxplots with an interval of 20th percentile of the simulation-based runoff ratio vs storm total precipitation and ASM of POTN3P (extreme precipitation events with threshold set to 3 events per year on average) in three basins during WY 1951–2000. The tick labels of x axis show the 0–20th, 20th–40th, 40th–60th, 60th–80th, and 80th–100th percentiles of either storm precipitation or ASM values.

In our previous analysis, we also showed that when ASM is high or storm precipitation is sufficiently large, extreme precipitation events can lead to extreme discharge events of the same POT threshold (see Fig. 12 in Cao et al. 2019). On the other hand, if the ASM is low, extreme precipitation may not lead to extreme discharge of the same POT threshold. In the following section, we examine whether the future increases in storm precipitation could outweigh the effects of future changes in ASM and thus enhance the connection of extreme precipitation and extreme discharge.

c. GCM projected changes

1) Changes in AR contribution to extreme precipitation and discharge events

We examined changes in AR contributions to the largest 50, 100, and 150 extreme precipitation (i.e., POTN1P, POTN2P, and POTN3P) and discharge (i.e., POTN1D, POTN2D, and POTN3D) events during the two periods, WY 1951–2000 and WY 2050–99, based on the ensemble mean of all 10 GCMs (see Table 1; the results based on the Real-5 GCMs are given in Table S5). The POT thresholds generally increased for both extreme precipitation and extreme discharge events in all basins (see Tables S3, S4). The percentage of AR-related POT extreme precipitation events changed by −2% to 1%, 0%–2%, and 5%–7% in the Chehalis, Russian, and Santa Margarita basins, respectively, while the percentage of AR-related POT extreme discharge events (AMF events) changed by −2% to 0%, 4%–6%, and 8%–9% (0%, 4%, and 8%) in the three basins, respectively. The change values seem to be small partly because 1) they are the ensemble mean of 10 GCMs (and can be much larger for individual GCMs) and 2) the sample size of extreme events we examined here is limited. Nonetheless, we can see that 1) the AR contribution to extreme events slightly decreases in the northernmost basin and increases toward south and 2) the changes are different for extreme precipitation and extreme discharge events.

2) Changes in the relationship of extreme precipitation to extreme discharge

Given that there were mismatches in the changes in AR contributions to extreme precipitation and extreme discharge events in the three basins, the relationship between extreme precipitation and extreme discharge might have changed. We examined future changes in the probability of extreme precipitation events leading to extreme discharge events of the same POT threshold by comparing the full GCM ensemble during the periods WY 1951–2000 and WY 2050–99. At basin outlets (see Figs. 5a,d,g), the relationship is projected to become significantly stronger (Wilcoxon signed-rank test, p < 0.1) in the Russian River basin for POT events of the highest threshold, and likewise in the Santa Margarita River basin for POT events of all thresholds. However, there is no statistically significant change at the Chehalis basin outlet for any threshold, and in fact, the relationships for POT events of the higher two thresholds are projected to slightly decrease.

Fig. 5.

Boxplots of the probability of extreme precipitation events leading to extreme discharge events of the same POT thresholds based on GCM ensembles during the periods of WY 1951–2000 and WY 2050–99. The p values of the Wilcoxon signed-rank test are shown in plots, with values less than 0.1 marked in bold font.

Fig. 5.

Boxplots of the probability of extreme precipitation events leading to extreme discharge events of the same POT thresholds based on GCM ensembles during the periods of WY 1951–2000 and WY 2050–99. The p values of the Wilcoxon signed-rank test are shown in plots, with values less than 0.1 marked in bold font.

To eliminate the impact of drainage area (denoted as DA) differences in the three basins (as well as the potential impact of the relationship between peak daily precipitation and storm total precipitation), we further examined the probability of changes at the subbasin level. Specifically, we examined subbasins with DAs above (see Figs. 5b,e,h) or below (see Figs. 5c,f,i) 1000 km2, a threshold used in previous studies (e.g., Ivancic and Shaw 2015; Wasko and Sharma 2017; Wasko and Nathan 2019). The pattern of changes across three basins was generally similar to those at the basin outlets.

Based on the analysis of historical events in the Russian River basin (Cao et al. 2019), we hypothesize that there are two ways that the future relationship between extreme precipitation and extreme discharge may be strengthened. One is through increases in storm precipitation; hence we first examined the role of ARs since they are usually associated with the most extreme precipitation events. The other is that large storms and wet ASM conditions are more likely to be concurrent; hence, we examined the ASM and timing of extreme precipitation events.

(i) Role of ARs

Figure 6 shows the probabilities conditioned on ARs (i.e., when extreme precipitation events were associated with ARs). The probabilities were higher in all three basins during each of the two periods (WY 1951–2000 and WY 2050–99) in comparison with no conditioning. For the projected changes in Pr(POTD|POTP), there was still no statistically significant change in the Chehalis basin, but the Pr(POTN1D|POTN1P) slightly increased in terms of the median value. In the Russian basin, the increase of Pr(POTN1D|POTN1P) was still statistically significant at the basin outlet, and there were more statistically significant changes in subbasins for POT events with lower thresholds, indicating that the changes in AR-related storm precipitation were sufficiently large to lead to more extreme discharge of the same POT threshold. In the Santa Margarita basin, the probabilities still increased both at the basin outlet and within subbasins, but less significant increases were possibly due to the relatively small number of AR-related extreme precipitation events in this basin.

Fig. 6.

As in Fig. 5, but for extreme precipitation events conditioned on ARs.

Fig. 6.

As in Fig. 5, but for extreme precipitation events conditioned on ARs.

(ii) Role of ASM

Whether an extreme precipitation event could lead to an extreme discharge event depends not only on storm total precipitation but also on ASM. We first examined the average changes in storm precipitation and ASM of POTN1P events (see Figs. 7a,b). The changes of POTN2P and POTN3P events are similar (see Fig. S1) and hence are not shown here. Storm precipitation generally increased in all three basins across all three POT thresholds, but the average changes in ASM varied among basins.

Fig. 7.

CDF of (a) storm total precipitation, (b) ASM conditions, and (c) storm occurrence date in three basins during POTN1P events (i.e., extreme precipitation events with thresholds set to 1 event per year on average). (d),(e) As in (a) and (b), but sorted by storm occurrence dates. (f) Seasonal cycle of the surface-layer soil moisture. (g) CDF of occurrence date of POTN1D events (i.e., extreme discharge events with thresholds set to 1 event per year on average).

Fig. 7.

CDF of (a) storm total precipitation, (b) ASM conditions, and (c) storm occurrence date in three basins during POTN1P events (i.e., extreme precipitation events with thresholds set to 1 event per year on average). (d),(e) As in (a) and (b), but sorted by storm occurrence dates. (f) Seasonal cycle of the surface-layer soil moisture. (g) CDF of occurrence date of POTN1D events (i.e., extreme discharge events with thresholds set to 1 event per year on average).

We further examined the seasonal timing of the POT events. The mean occurrence date of historical POTN1D events became later progressing from north to south along the coast: around mid-January in the Chehalis basin, late January in the Russian basin, and early February in the Santa Margarita basin (see Fig. 7g). Extreme discharge events historically occurred from mid-November to mid-March in the Chehalis basin when the soil was relatively wet and when it was close to the peak of the AR season in the Pacific Northwest region (Gershunov et al. 2017). Sorting the POTN1P events by their occurrence dates shows that low ASM occurs more often during the shoulder seasons (see Fig. 7e), which is projected to become even lower, possibly due to precipitation frequency loss (see Fig. 7f for the seasonal cycle of soil moisture). Large storms are projected to occur more often in the late fall in the Chehalis River basin (see Figs. 7c,d), which will be increasingly strongly affected by carry-over (dry) ASM from the previous summer, and are increasingly unlikely to lead to extreme runoff events. In contrast, large storms are projected to occur more often in the winter in the Russian and Santa Margarita River basins due to the delayed onset of winter precipitation in California (Pierce et al. 2013b).

We examined the Rs between storm precipitation and ASM during POT extreme precipitation events (see Fig. 8). There is no clear pattern of changes in Rs in the Chehalis River basin. The Rs is generally projected to increase, however, in the Russian and Santa Margarita River basins for POTN2P and POTN3P events. Nearly half of the Rs values are projected to be significant (p < 0.1) among 10 GCMs, indicating that a large storm is projected to be more likely to follow a wet ASM condition as the year-to-year volatility of annual precipitation is projected to increase in California. AR events tend to produce higher runoff in comparison with non-AR events not only by producing heavy precipitation but also by suppressing evapotranspiration (ET) and hence modulating ASM (Chen et al. 2019). This could also be related to changes in AR families, which are rapid succession of individual AR events occurring within one week (Fish et al. 2019). The average number of winter AR families is projected to slightly increase in all three basins based on 10 GCMs, with greater increases in the Russian River basin. We intend to further examine the impact of AR families in future work.

Fig. 8.

The Spearman’s rank correlation Rs between storm precipitation and ASM during POTN1P, POTN2P, and POTN3P events (i.e., extreme precipitation events with thresholds set to 1, 2, and 3 events per year on average). The correlations with p values less than 0.1 are shown as solid symbols.

Fig. 8.

The Spearman’s rank correlation Rs between storm precipitation and ASM during POTN1P, POTN2P, and POTN3P events (i.e., extreme precipitation events with thresholds set to 1, 2, and 3 events per year on average). The correlations with p values less than 0.1 are shown as solid symbols.

3) Changes in AMF flows

We next examine changes in the magnitude of AMF events, as well as storm precipitation and related ASM based on the ensemble mean of all 10 GCMs (see Fig. 9). The magnitude of AMF and its storm precipitation showed an overall increase in all three basins. The AMF (averaged over 50 years) increased by 21%, 29%, and 48% in the Chehalis, Russian, and Santa Margarita basins, respectively. Storm precipitation increased by 17%, 26%, and 38%. However, the ASM preceding future AMF events is not projected to change much—2%, 1%, and −2% on average (but varying among individual events) in the three basins.

Fig. 9.

CDF of peak daily flow, storm total precipitation, ASM conditions, and event occurrence dates during AMF events, as well as annual precipitation in (a) Chehalis River basin, (b) Russian River basin, and (c) Santa Margarita River basin based on the ensemble of 10 GCMs.

Fig. 9.

CDF of peak daily flow, storm total precipitation, ASM conditions, and event occurrence dates during AMF events, as well as annual precipitation in (a) Chehalis River basin, (b) Russian River basin, and (c) Santa Margarita River basin based on the ensemble of 10 GCMs.

We examined what caused the changes in ASM in the three basins, specifically changes in temperature versus changes in antecedent precipitation. Figure 10 shows the correlation between the changes in soil moisture (ΔSM) and antecedent precipitation (P) given ET (denoted as rΔSM&P-ET), and the correlation between ΔSM and ET given P (denoted as rΔSM&ET-P) for different pre-event durations (from 2 days to 12 weeks). The figure shows that the ASM are affected more by P than ET in each of the basin during both periods, except for the Chehalis basin during WY 1951–2000 due to the influence of seasonality of events. When they are examined in separate seasons (i.e., late fall and winter), the relative influence of P is larger than ET. For the Santa Margarita basin in Southern California where the projected loss of non-AR precipitation frequency is most pronounced (Polade et al. 2014, 2017; Gershunov et al. 2019), the correlation between ΔSM and P given ET is much higher than in the other two basins, indicating that precipitation regime change is a relatively more important driver of projected ASM changes in this basin. In the meantime, the relative influence of ET on ΔSM is smallest in the Chehalis basin, intermediate in the Russian basin, and largest in the Santa Margarita basin. Warming shows a greater impact on ASM of AMF events in the Santa Margarita basin, where the change in precipitation regime may exert a synergetic effect that makes the ASM more vulnerable to warming.

Fig. 10.

Effects of temperature and antecedent precipitation conditions on changes of antecedent soil moisture (ΔSM) of annual maximum flow events in (a) the Chehalis River basin, (b) the Russian River basin, and (c) the Santa Margarita River basin, which are examined as correlation between ΔSM and accumulated precipitation P given accumulated ET, and correlation between ΔSM and ET given P under different pre-event durations. (top) The period of WY 1951–2000 and (bottom) the period of WY 2050–99.

Fig. 10.

Effects of temperature and antecedent precipitation conditions on changes of antecedent soil moisture (ΔSM) of annual maximum flow events in (a) the Chehalis River basin, (b) the Russian River basin, and (c) the Santa Margarita River basin, which are examined as correlation between ΔSM and accumulated precipitation P given accumulated ET, and correlation between ΔSM and ET given P under different pre-event durations. (top) The period of WY 1951–2000 and (bottom) the period of WY 2050–99.

We further examined the correlation between storm precipitation and AMF given ASM (denoted as rSP&AMF-ASM), and correlation between ASM and AMF given storm precipitation (denoted as rASM&AMF-SP) during WY 1951–2000 and WY 2050–99 (see Fig. S2). The rASM&AMF-SP is projected to slightly decrease (p < 0.1 in the Wilcoxon test) among GCMs, yet still significant, in the Russian River basin where storm precipitation is projected to increase most among three basins caused by ARs. No statistically significant changes were found in the other two basins.

5. Discussion

We examined changes in the magnitude of extreme floods with return periods of 4, 10, 20, 50 and 100 years by fitting the generalized extreme value (GEV) distribution to the AMF from each GCM. Mallakpour et al. (2019) evaluated different distributions for the AMF in multiple basins in California and found the GEV performed best in most cases. We used the Wilcoxon test to determine whether there is any significant change in the percent change of flood magnitude between WY 2050–99 and WY 1951–2000 (see Fig. 11). The full GCM ensemble projects significant increases (p < 0.1) in the magnitude of floods with all return periods in all three basins. The Real-5 GCMs project significant increases (p < 0.1) in the 4-, 10-, 20-, and 50-yr floods in the Chehalis River basin; the 4-, 10-, and 20-yr floods in the Russian River basin (despite of much higher median values for all extreme floods in comparison with the full GCM ensemble); and floods with all return periods in the Santa Margarita River basin, where the fraction of AR-related AMF events increases most.

Fig. 11.

Percent change in 4-, 10-, 20-, 50-, and 100-yr recurrence interval flow between WY 2050–99 and WY 1951–2000 based on (top) the ensemble of 10 GCMs and (bottom) the Real-5 GCMs. The p values less than 0.1 are marked in bold font.

Fig. 11.

Percent change in 4-, 10-, 20-, 50-, and 100-yr recurrence interval flow between WY 2050–99 and WY 1951–2000 based on (top) the ensemble of 10 GCMs and (bottom) the Real-5 GCMs. The p values less than 0.1 are marked in bold font.

To examine how representative the three basins are of a broader set of U.S. West Coast watersheds, we identified a set of unregulated coastal basins for which we examined their hydroclimatic conditions. We selected 60 coastal USGS stream gauges used by Stewart et al. (2005) (see gauge locations in Fig. 1d). These watersheds are all relatively unaffected by anthropogenic influences. For each watershed, we calculated the mean annual precipitation (averaged over its drainage area) and winter (November–March) precipitation ratio averaged over WY 1951–2000 using gridded precipitation from Livneh et al. (2015). We also calculated a flood ratio (i.e., average of the annual maximum daily flows divided by the annual runoff) using the stream gauge records. Figures 1e–g show these characteristics versus latitudes (see the number of winter AR events versus latitudes in Fig. S3), which show systematic geographic variations in the hydroclimatic conditions along the transect within which our three target catchments lie.

While the analysis above indicates that the three basins we studied are indeed representative of hydrologic conditions along the transect, we note here some other limitations of our study. One is that there are uncertainties in the AR-related percentages of extreme events we report, partly related to the low spatial resolution of both the NCEP–NCAR reanalysis dataset and the GCMs that the AR date catalogs are based on. However, Ralph et al. (2018) showed that the derived landfalling AR catalogs were not sensitive to reanalysis spatial resolution (this was specifically for the Russian River basin) but were much more sensitive to the detection methodology used. Also, the SIO-R1 catalog we used here as well as the algorithm used to produce the catalog are highlighted by Ralph et al. (2018) and Chen et al. (2018, 2019). They note that the catalog is especially relevant for precipitation-related studies as it was specifically developed with precipitation applications in mind and evaluated with independent precipitation data (Gershunov et al. 2017). In terms of the uncertainties in percentages based on GCMs, rather than their spatial resolution, a greater uncertainty appears to be GCM biased in IVT—mostly wet, but some unrealistically dry, as is discussed in Gershunov et al. (2019). Hence we focused more on changes in the percentages than their absolute values.

Other limitations are related to meteorological forcings. For example, Pierce et al. (2013a) showed that the algorithm we used here to estimate humidity might not preserve the original global model’s humidity trends, but they also pointed out that coastal areas were less biased than the interior areas. Additionally, the hourly precipitation disaggregation method is a potential source of uncertainty. We evaluated the MoF method (from Westra et al. 2012) we used in comparison with (commonly used) uniform apportionment in one of the catchments. Since our analyses are mostly based on daily data, the choice of disaggregation method has somewhat limited influence on our results (see results in Fig. S4). Moreover, some studies have shown the impact of warming on the intensification of precipitation at subdaily time scales (e.g., Westra et al. 2013, 2014; Meredith et al. 2019), indicating that atmospheric states may need to be considered in the precipitation disaggregation for the future period, which will be included in our future work.

6. Conclusions

Recent studies have projected large increases in AR activity along the U.S. West Coast, which are projected to be associated with increases in extreme precipitation events during the twenty-first century (e.g., Hagos et al. 2016; Warner et al. 2015; Gershunov et al. 2019). However, ASM is a primary link between precipitation and flooding, and might decrease with projected decreases in prestorm low- to medium-intensity precipitation frequency, and increased antecedent evaporative demand associated with warming. This would mitigate the flood response to increases in extreme precipitation caused by ARs. Here, we first examined changes in AR contributions to the largest 50, 100, and 150 extreme discharge events in three rain-dominant basins along the U.S. West Coast in two periods, WY 1951–2000 and WY 2050–99. We then examined how ASM is likely to change in the future and assessed its effect on changes in flood response. Based on our analysis, we find the following:

  1. Historically most extreme discharge events in all three river basins have been AR-related (specific fractions depend on the POT threshold used and the specific river basin). In a warmer climate the projected fraction of AR-related extreme discharge events will decrease slightly in the northernmost (Chehalis River) basin but will increase toward south. It will increase in the Russian River basin and even more in the southernmost Santa Margarita River basin. The increases in the two California basins are due to increases in the fraction of AR-related extreme precipitation events, as well as changes in the relationship between (AR-related) extreme precipitation and extreme discharge. The relationship is projected to be significantly stronger (for certain POT thresholds) partly due to large storms being more likely to follow wet ASM conditions as the year-to-year volatility of annual precipitation is projected to increase in California.

  2. The ASM associated with AMF events is projected to slightly increase (on average) in the Chehalis and Russian River basins, while the ASM is projected to slightly decrease (on average) in the Santa Margarita River basin, where the loss of non-AR precipitation frequency is most pronounced and hence it may exert a synergetic effect that makes the ASM more sensitive to warming. The influence of changes in ASM on AMF varies among individual events in three basins, but the relative effect of ASM on AMF given storm precipitation is projected to become slightly weaker (yet still significant) in the Russian River basin where storm precipitation is projected to increase most among three basins caused by ARs.

  3. In terms of the general changes in extreme floods, the full GCM ensemble projects significant increases in the magnitude of 100-yr floods in all three basins. The “Real-5” (five most realistic GCMs in terms of their reproduction of historical AR event statistics) project relatively small increases in the magnitude of the 100-yr flood in the Chehalis River basin but greater increases in the Russian and Santa Margarita River basins than the full GCM ensemble. Their projections show that, however, the increase in the magnitude of 100-yr flood is only significant in the Santa Margarita River basin, where the number of AR-related AMF events increases most and the year-to-year volatility of annual precipitation is projected to increase most. Differences in the results indicate that caution is needed in the selection of GCMs for future flood analyses in these basins.

Acknowledgments

The research supported herein was funded by the Center for Western Weather and Water Extremes (CW3E) at the Scripps Institution of Oceanography UC San Diego via AR Program Phase II, Grant 4600013361, sponsored by the California Department of Water Resources.

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