On 3 December 2007, an atmospheric river (AR) event made landfall on the West Coast of the US. The resulting extreme precipitation event severely impacted the Chehalis River basin in western Washington and resulted in 6 h rainfall amounts close to the 100-year storm volume . Two previous storms (on 1 and 2 December) brought heavy snow to the Oregon Coastal Range, the Olympic Mountains and the Cascades, while the third and strongest event brought mostly liquid precipitation. The hurricane force winds on 3 December produced wind damage with tree blowdowns, power outages, huge ocean swells and a record coastal storm surge. Eleven people lost their lives. Millions of people lost power throughout Washington and Oregon as a result of the storm. Portions of interstate 5, the major north–south freight corridor on the West Coast connecting the Puget Sound region of Washington with Oregon and California, were closed for 4 days, resulting in an estimated USD 47 million in economic losses . Major disaster declarations were issued in several counties in the states of Washington and Oregon, but most of the damages were concentrated in three counties in Washington: Grays Harbor, Lewis and Thurston. Lewis County, within which the most affected part of the Chehalis River basin lies, experienced the largest impact with USD 166 million in damages and 46 % of its agricultural land flooded .

While this event was particularly extreme, more than 50 % of the total cool-season precipitation and more than 75 % of heavy precipitation (top decile) on the west coast of Oregon and Washington is related to AR events . Water vapor transport during the winter season is often roughly orthogonal to the mountain ranges, which favors orographic precipitation enhancement . Furthermore, ARs with anomalous warm, strong, low-level water vapor fluxes are responsible for nearly all of the extreme flooding along the Olympic Mountains and the western Cascade Mountains of Washington .

Given the critical role of ARs for precipitation and flooding in the region, it is important to understand how these could change in a warmer climate. As tropospheric temperature increases, integrated water vapor transport (IVT) is projected to increase by 30–40 % by the end of the 21st century along the North Pacific storm tracks, including the West Coast of the US . In climate model projections, years with many AR storms are projected to become more frequent and water vapor content is projected to increase during intense AR events . The changes in IVT are driven mostly by thermodynamics through increased water vapor content of a warmer atmosphere, while changes in dynamics seem to have only a secondary effect along the northern West Coast of the US . Based on the analysis of IVT changes, it is tempting to conclude that the projected increase in intensity and frequency of AR events will lead to increased flooding in the region. However, to quantify the risk of inundation and its economic impact, it is important to understand the myriad of processes that happen between the impact of an AR in a watershed and the resulting flooding.

In this work, we present an integrated modeling system that quantifies the atmospheric–hydrologic–hydraulic and economic impacts of the December 2007 AR event. In addition, we use the modeling system to project physical and economic impacts under a scenario in which the same December 2007 event occurs in an atmosphere with increased greenhouse gas forcing. As opposed to a traditional approach that uses an ensemble of downscaled and bias-corrected climate model simulations, we use the regional model simulations of the December 2007 event in hypothetical future climate settings. We then use these high-resolution simulations in a warmer climate as forcing for the hydrology–hydraulic and economic loss models. Our work follows a similar procedure as the US Geological Survey (USGS) Multihazards Project, which used a synthetic but plausible California AR scenario to estimate the human, infrastructure, economic and environmental impacts for emergency preparedness and flood planning exercises . In our work, we focus on the Chehalis River basin in western Washington to provide an end-to-end model of severe weather, physical impacts and economic consequences of ARs in a warmer climate.

The integrated modeling system allows us to incorporate different types of uncertainty, including (a) alternative future radiative forcings associated with different Coupled Model Intercomparison Project 5 (CMIP5) Representative Concentration Pathways (RCPs) – RCP4.5 and 8.5 scenarios, (b) different possible responses of the climate system to future radiative forcings as represented by 14 CMIP5 GCMs and (c) different possible responses of the hydrologic system as represented by two different hydrologic models. We do not account for possible changes or structural failures in the main channel hydraulics and we do not account for possible changes in private or public building infrastructure or trade flows. At each step in the modeling chain we provide an envelope of possible future responses of the system and present them as changes with respect to the historical control simulation. The modeling system is intended to provide decision makers with information about the range of physically plausible changes in flood-causing AR storms and floods, as well as a tool to quantify the related economic impacts.

The Chehalis River basin, with a drainage area of approximately 5400 square kilometers, is located in southwest Washington state (Fig. b). It heads in the Willapa Hills, flows east, then north and west into Grays Harbor. Most of the basin lies below 1000 m of elevation. Fall and winter precipitation mostly occurs as rain, with exceptions in small areas of the extreme northern and eastern portions of the basin. Floods in the basin generally occur in late fall and early winter and are associated with atmospheric rivers. The most significant floods in the observational period are January 1972, January 1990, November 1990, February 1996, December 2007 and January 2009 (USGS). We focus on the largest event recorded in the basin, the December 2007 event.

On 3 December 2007, an AR filamentary plume transporting more than 2000 kgm-1s-1 of water vapor at its core extended from the tropical Pacific, west of Hawaii, to the coast of Oregon and Washington (Fig. a). Selecting the cross section of the AR with the most intense transport and integrating IVT for all values exceeding 1500 kgm-1s-1, we can calculate the equivalent liquid water discharge. This AR carried approximately 847 000 m3s-1 of liquid water across its inner core, or the equivalent of about 50 times the average discharge at the mouth of the Mississippi River. Temperatures rose 17 ∘C in less than 2 h ahead of the cold front . Along this warm southwesterly tropical air mass, more than 70 % of the water vapor and precipitation that reached the coast was of direct tropical origin . The catastrophic flooding along the Chehalis River basin was primarily due to unusually high and sustained hourly rainfall rates concentrated in less than 24 h, mainly on 3 December. The conditions were exacerbated by warm air advection into the region by the AR, which produced rain on snow conditions and partially melted the existing shallow, low-elevation snow. Ten USGS stream gauges experienced record flooding, including four on the Chehalis River or its tributaries (Grand Mound, Porter, Doty and South Fork Chehalis; see Fig. b for station locations). The peak discharge measured at Doty was a 500-year event – the only 500-year stream peak event ever recorded in western Washington.

The WRF-control simulation captures the observed extreme precipitation over the Oregon Coastal Range and Olympic Mountains with precipitation on the order of 80 mmday-1 over some areas (Fig. a and b). However, the simulation overestimates precipitation over the Cascades and underestimates precipitation over most of the Chehalis basin by about 30–40 % (Fig. c). The simulation overestimates precipitation over the Willapa Hills in the southern part of the basin.

HEC-HMS captures the timing of peak stage and flow; however, it has problems with underestimation of peak flow and more generally underestimates discharge throughout most of the basin (Fig. , dashed lines). DHSVM, on the other hand, adequately captures peak flow in the upper basin (Doty and Newaukum), but overestimates peak discharge in the lower basin and underestimates recession flows (Fig. , dotted lines). As explained above, the hydrologic models are different and they both have strengths and weaknesses in simulating different parts of the hydrograph at different locations. It is important to note that we used a combination of Livneh precipitation data (daily timescale) with hourly data from five NOAA stations (shown in Fig. ) to partition the Livneh daily totals. Hence, while the total daily volumes match the Livneh product, the hourly variability comes from the station data. There is considerable uncertainty in the Livneh precipitation product daily totals for this storm and even more uncertainty as to the hourly precipitation throughout the basin. Errors in the hydrologic response are largely due to error in the precipitation estimates. Since the 2007 flood, an NWS precipitation radar has been installed (at Langley Hill) and the number of HADS stations has increased, helping to better resolve the space–time distribution of precipitation over the basin. These assets were not, however, available during the 2007 storm.

The calibrated HEC-RAS hydraulic model, driven by observed streamflow from the USGS stations (see station locations in Fig. ), performs very well (Fig. ). The differences between the observed and simulated stage along the Chehalis main stem range from -0.54 to 0.65 m, while the difference in peak flow magnitude ranges from about -1.4 % at Doty (upstream) to -16.9 % at Porter (downstream). The resulting inundation depth and extent are shown in Fig. . Large areas around the cities of Chehalis and Centralia (see Fig. b for location) were inundated.

We used the inundated areas and depths from HEC-RAS to calculate the local damages to arable land, buildings and content, infrastructure and vehicles using HAZUS. Then the net loss in local production is calculated using the Inv-DIIM. The total physical damages for Lewis, Thurston and Grays Harbor combined were estimated at USD 678 million with business disruption losses of USD 51 million (Table , “Base” rows), most of which was in Lewis County (Avelino and Dall'erba, 2016). While reported loss estimates are difficult to obtain, the Department of Commerce estimated that losses for the states of Washington and Oregon combined for this flooding event were approximately USD 1 billion, so our estimates for the three counties seem reasonable. In addition, the official building and inventory damages in Lewis county were estimated at USD 166 million , which is close to our estimate of USD 151 million for the same categories. It is important to clarify that we do not have a counterfactual that can be used to calibrate the economic model in the same way that we calibrate the physical models.

Overall, we find that individually the models of the integrated system realistically capture the dominant physical and economic processes. However, it is clear that there are problems with some variables, particularly precipitation and the associated hydrologic response. For this reason, we decided not to use the raw model output (from WRF, HEC-HMS, DHSVM or HEC-RAS) to drive the subsequent model in the historical simulations (Fig. ). Instead, we use the individual historical model simulations forced with observations. To simulate the climate change response, the observations are then multiplied by a factor that accounts for the changes projected by the models in a warmer climate (as described in Sect. ). The underlying idea is that the models cannot provide precise spatiotemporal values of the different variables; however, because their representation of the dominant processes is realistic, we trust they are able to capture the changes between the past and the future. This is the rationale behind the “delta method”.

In the WRF-PWG simulation, we added the changes in temperature shown in Fig.  (both upper and lower scenarios) to each level of the ERA-Interim boundary conditions used in the control simulation, while maintaining constant relative humidity. This necessarily implies an increase in the specific humidity, as higher temperatures increase the saturation specific humidity. These changes induce variations in the IVT of the projected AR event, which increases by 12.6 % in the lower scenario to 38.5 % in the upper scenario for the WRF outer domain (Fig.  shows the spatial changes for the PGW-upper scenario). The increase approximately follows the Clausius–Clapeyron scaling of about 7 % per degree of warming. The increase in IVT can be as large as 500 kgm-1s-1 throughout the AR corridor. IVT also increases within the inner WRF domain by 12.4 to 42.3 % for the two scenarios. The water vapor mixing ratio increases everywhere, but not homogeneously in space (Fig. a), with a clear structure of changes above 40 % at the 800 mb level. However, due to the differences in temperature, the relative humidity can increase or decrease in the PGW-upper simulation, and this leads to both positive and negative changes in the cloud water mixing ratio (Fig. b and c). In Fig.  we show these results at the 800 mb level, but these heterogenous changes in relative humidity and cloud water can be seen throughout the lower troposphere. As a consequence, precipitation shows both areas of significant increase and decrease throughout the WRF inner domain. The inner domain area-averaged precipitation change is 8.2 % for the lower scenario and 17.8 % for the upper scenario – significantly below the Clausius–Clapeyron scaling. On the basin scale, precipitation increases significantly (exceeding 30 %) in the northern part of the watershed and deceases significantly (below 30 %) in the southeastern Chehalis basin (Fig. e). We calculated the fractional changes in precipitation for each sub-watershed as the total precipitation that accumulated between 1 and 4 December of the WRF-PGW simulation divided by the WRF-control accumulated precipitation for the same period (Fig. a). The upper basin (lowest sub-basin numbers) clearly shows precipitation increases, the eastern part of the basin shows decreased precipitation and the lower basin shows increased precipitation.

We multiplied the observed precipitation by the fractional change in precipitation (shown in Fig. a for each HEC-HMS sub-basin) and used the result to force the HEC-HMS and DHSVM PGW simulations. There are two different scenarios that result in four different hydrologic simulations (HEC-HMS-lower, HEC-HMS-upper, DHSVM-lower and DHSVM-upper). The results show that some regions generate significantly more runoff due to increased precipitation, while the southeastern part of the basin generates less runoff (Figs. b and ). Notably, the Doty station in the headwaters of the basin shows an increase in peak runoff that ranges from 13 % in DHSVM-lower to 44 % in HEC-HMS-upper. The use of the two hydrologic models provides an envelope of uncertainty in the numerical representation of the hydrologic response (Fig. ). We find that the sharp increase in streamflow in the headwaters dominates the response in the main channel, as simulated by HEC-RAS (Fig. ). There is an increase in both stage and flow throughout most of the channel, with increases that range from about 12–42 % in the headwaters (depending on the scenario), to -6 to 5 % in the eastern part of the basin and then about 10–30 % at the outlet into Grays Harbor (Figs.  and a). Only the DHSVM-lower scenario shows small decreases in the eastern part of the basin.

The associated socioeconomic losses, as simulated by HAZUS and Inv-DIIM, show an increase in physical damages of 2–33 % in Grays Harbor County, 9–171 % in Lewis County and -1–10 % in Thurston County. The results are sensitive to the scenario and the hydrologic model used (Table ). Our results indicate a larger loss in Lewis County because it is where Centralia and Chehalis, two of the most populated cities of our watershed, are located and they hold the largest stock of private and public buildings and infrastructures. Interestingly, in terms of business interruption losses (Table  lower), the increases are substantially higher and can be very different from the changes in physical damages (27–250, 14–314 and 46–619 %, respectively). The economy outside of these three counties (“Rest of WA”, Table 3) is positively impacted as reconstruction and recovery efforts stimulate production in the rest of Washington. As a result, the net impact on local statewide production and internal trade is positive.

ARs are responsible for most of the extreme winter flooding events in the western US. As the climate warms, the thermodynamic response of these atmospheric structures will likely lead to significantly more water vapor content and fluxes. Others have hypothesized that a warmer climate will lead to more intense AR-related flooding events and societal impacts. However, the way that the water vapor carried by an AR is transformed into precipitation, runoff and streamflow along a channel is highly nonlinear and depends on a myriad of fine-scale processes both in the atmosphere and on the land surface. Furthermore, the economic impacts depend on the human footprint, the economic structures in the affected areas and their trade linkages with other regions. Because of the risk associated with these events, we need appropriate tools to assess the physical and economic impacts of ARs in a warmer climate.

We have presented an integrated modeling tool that tracks an AR from its atmospheric development to the economic impacts related to inundation and flooding. We have used this tool to understand how the ARs and their impact could change in a warmer climate using a PGW approach. As argued by , this type of approach is particularly useful for the affected communities because it uses high-resolution models to simulate an extreme hydrologic event that occurred in the past and that the community can remember. The method is flexible enough to tailor the projections to a narrative; in this case, how would this extreme event change in a warmer climate? Furthermore, the method takes into account three types of uncertainty: (a) uncertainty in future radiative forcing, (b) uncertainty in the climate system response to this radiative forcing and (c) uncertainty in the hydrologic response of the system. In this way, we provide the community with a range of uncertainty of possible future conditions.

In our application to the December 2007 AR flooding event over the Chehalis River basin, we found that while there is a clear intensification of AR specific humidity and integrated vapor transport for both the lower and upper PGW scenarios, these changes do not translate into generalized increases in precipitation throughout the basin due to spatially heterogeneous changes in relative humidity and water vapor mixing ratio. For this reason, some parts of the basin receive more precipitation, while others receive less. These changes in precipitation translate into amplified changes in sub-basin runoff (in terms of percent change in water mass). But, because the upper basin runoff increases substantially, the streamflow along most of the Chehalis main stem increases in the warming scenarios. Interestingly, this event was so large that even in the control simulation most of the inundated area was occupied. As a consequence, while the PGW simulation resulted in significant changes in inundation depth, changes in the inundated area were minor. However, these changes in flood depth resulted in economic losses due to stock damages that ranged between -1 and 171 %, while losses in local production and trade within the three impacted counties were between 14 and 619 % (depending on the affected county, PGW scenario and hydrologic model). The economy outside of these three counties actually benefited from the event as it provided the entirety of the reconstruction efforts after the flood. Because the 2007 event was so rare, we also offer a simplified way to estimate the economic losses associated with floods with a shorter return period and calculate changes in expected annual losses.

The meteorology and hydrology combined with public policy and mitigation cost–benefit considerations will remain a difficult challenge in the future for the Chehalis basin. Flooding potential may need to be reconsidered in light of possible changes in atmospheric rivers in a warmer climate. Our integrated modeling tool provides communities in the Chehalis region with a range of possible future physical and socioeconomic impacts associated with AR flooding. The framework takes into consideration several important sources of uncertainty. It can be applied to other intense flooding events that perhaps affected other parts of the basin. Furthermore, the tool can be modified to understand different future scenarios, including the failure of hydraulic structures and changes in land use and land cover. In this way, communities in the region will be better prepared to mitigate the losses and improve disaster relief efforts associated with likely changes in precipitation and flooding that a warmer climate will bring.