Climate change alters flood magnitudes and mechanisms in climatically-diverse headwaters across the northwestern United States

We used a large ensemble of hydrologic and climate change simulations for the Pacific Northwestern United States spanning the years 1950 to 2099 (Chegwidden et al 2017) as our base dataset. As described in Chegwidden et al (2019) and River Management Joint Operating Committee (RMJOC; 2018), the simulations represent a multi-model ensemble of hydrologic projections, which is useful for representing uncertainty in projections of the hydrologic response to climate change (Velázquez et al 2013, Bosshard et al 2013, Vormoor et al 2015, Vano et al 2018). The climate projections from two representative concentration pathways (RCPs; mid-range greenhouse gas concentrations, RCP 4.5; high concentrations, RCP 8.5) and 10 global climate models (GCMs) were selected from the Coupled Model Intercomparison Project Phase 5 (Taylor et al 2012). Climate data were downscaled to the spatial resolution of the hydrologic models (∼6 km) using the Multivariate Adaptive Constructed Analogs (MACA) downscaling method (Abatzoglou and Brown 2012). The four hydrologic models include three implementations of the Variable Infiltration Capacity model (i.e. VIC-P1, VIC-P2, VIC-P3) and one implementation of the Precipitation Runoff Modeling System (PRMS-P1). The resulting hydrologic simulations do not account for changes in land use or landcover, which can influence hydrologic extremes (Bronstert et al 2002, Blöschl et al 2007, Hurkmans et al 2009). Further details on the construction of the dataset can be found in the Supplementary Materials Section S2.1.

Most analyses to date have used gridded runoff fields taken directly from climate or hydrologic model simulations without routing this runoff through a stream network. We used routed streamflow instead to assess the aggregate impact at locations in the stream network. We analyze headwaters basins (most smaller than 4500 km² and all smaller than 10 000 km²) because floods in small basins likely result from a single dominant FGP. Although, our results are based on unregulated flows and do not account for local channel and floodplain hydraulics, the findings can still aid in informing future management.

We conducted our study across 21 headwater basins located in a variety of hydroclimates within the Pacific Northwestern United States (PNW, see figure 1). Broadly, this region is characterized by winter-dominant precipitation (Hamlet et al 2013). The precipitation falls on regions of complex topography and temperature regimes, creating a spectrum of snow-rain partitioning across the region. The range of hydroclimates creates a variety of ways in which snow mediates the timing of runoff in the region.

As a representative sample, we distributed the 21 headwaters basins among three different hydroclimatic regions: (1) temperate: seven basins in lowland, historically rain-dominant regions, (2) transition: seven basins on the leeward side of the Cascade Range, characterized as basins where precipitation is projected to transition from snow to rain under climate change, and (3) montane: seven high-elevation basins in the Rocky Mountains where snow is generally less at risk under a changing climate (Hamlet et al 2013, Chegwidden et al 2019).

Our analyses focus on annual maximum flow (AMF) from 1951–2099. We calculated AMF as the highest daily flow to occur in any given water year (WY, October 1—September 30). We specifically selected AMF as opposed to other high flow metrics to create a single value for each year in the study period. As if often done in the literature, we will use the term 'flood' when discussing high flow, although we recognize that a river's AMF will not necessarily exceed its banks. Bankfull flow can be approximated by the one- to two-year return flow, and so the AMF is reasonably representative of flood stage.

We based our FGP classification generally on (Berghuijs et al 2016) but modified the classification scheme to better suit our objectives. We determined the FGP directly for each AMF event, which is in contrast to Berghuijs et al (2016) who inferred FGP by aligning the mean timing of streamflow events with the mean timing of precipitation and snowmelt events. Determining the FGP accounts for potential changes in upstream climate and hydrologic states, which is important in a climate change study.

We first collated the AMFs with their corresponding basin-average states and processes (i.e. precipitation and snowmelt) for each model simulation during the period (1950–2099). We then classified each AMF according to its dominant FGP via a decision tree. The definitions of each FGP and the classification tree are outlined fully in section S2.3. The FGP classes were: (1) Precipitation driven, (2) snowmelt driven, (3) Rain-on-snow (ROS) driven, (4) Other. We limited our analyses to a small number of classes to create a sufficiently large sample size for each classification in our sensitivity analysis (section 2.4). While many hydrologic events result from a diversity of upstream processes (e.g. rain-on-snow at high elevations combined with rain at low elevations), we identified a single, dominant FGP for each high flow event. Accordingly, we selected small headwater basins so that a single upstream FGP was likely to be dominant. We then identified how these FGPs changed under climate change.

For regional analyses, we calculated the portion of events for all simulations at all sites in each region caused by each FGP for each year. This paper does not focus on the 'Other' class as it played a minor role overall in our study basins.

Vano et al (2012) estimated sensitivities of annual streamflow to changes in annual temperature and precipitation. We instead estimated sensitivities of high flows to changes in annual temperature and precipitation and took the additional step of calculating sensitivities of high flow conditional upon their FGPs.

For each location, we calculated the ensemble's projected sensitivities to changes in annual temperature and precipitation according to the methods described in Section S2.4 of the Supplementary Materials. Changes in climate were calculated as changes in mean annual precipitation and temperature between the control (WY 1970–1999; '1980s') and future (WY 2070–2099; '2080s') periods for the area upstream of each streamflow location for the years classified by each FGP.

We purposefully base our analyses on annual changes in temperature and precipitation for three reasons. First, we want to provide a first-order understanding of the sensitivity of high streamflow to climate change to support stakeholders with limited access to computing resources or costly modeling studies. These first-order understandings also align well with the existing sensitivity literature which is often with respect to mean annual changes. A similar approach was used by Curry and Zwiers (2018) to investigate linear relationships between high flows and hydroclimate predictors based on the observational record. Second, we reiterate the reasoning of Curry and Zwiers (2018) that the annual precipitation signal is less variable than the daily extremes and thus the sensitivity approach is more robust when calculated upon the annual values.

As detailed in Queen et al (2019), AMFs are projected to increase across the PNW at almost all locations under any scenario, in general agreement with Tohver et al (2014). Projected changes in AMFs from all 396 streamflow locations in the Chegwidden et al (2019) ensemble are shown in figure S1 in the supplementary materials (available online at stacks.iop.org/ERL/15/094048/mmedia). As AMF magnitude (and, by proxy, basin size) decreases, the increases in AMF grow larger. The contrast between small and large basins aligns with the suggestion from Sharma et al (2018) that extremes will intensify more strongly in headwater basins. The significance of headwaters basins as being most susceptible to changes in flooding further motivates our focus on smaller basins in this study.

Snow accumulations are projected to decrease throughout the 21st century with projections of increased temperatures (Elsner et al 2010, Hamlet et al 2013, Gergel et al 2017, Chegwidden et al 2019). Accordingly, the overall role of snow in the hydrology of extreme events is projected to diminish, thus affecting the distribution of FGPs.

Changes in dominant FGP over time demonstrate the changing role that different processes play in the generation of AMF (figure 2). Figure 2(a) shows that in the 20th century AMFs in temperate basins were mostly precipitation driven, with about 25% caused either by snowmelt or rain-on-snow (though mostly snowmelt). By the 2080s under RCP 8.5, ROS-driven AMFs nearly disappear and snowmelt-driven AMFs account for only about 5% of the total.

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The transition basins will experience a large shift from snowmelt to precipitation-driven AMFs (figure 2(b)). In the 20th century, precipitation is responsible for only 13% of AMFs but by the end of the 21st century it is responsible for 50% of AMFs. Transition basins also experience a small reduction in the frequency of AMFs caused by ROS (23% to 18%). When compared to the results for RCP 4.5 shown in figure S2, we see that emission mitigation has its greatest impact in transition basins, where mitigation could reduce the share of precipitation-driven AMFs from 50% to 35% by the 2080s.

Our findings contrast with those of Musselman et al (2018) who found that, based on one set of simulations from one high-resolution climate model, ROS incidence would decrease at low elevation basins in the PNW but increase in higher elevation basins. In our study, we see a near disappearance of ROS AMFs in the temperate (lower elevation) region, and a small decrease in higher elevation regions (figure 3(c)), likely owing to the overall reduction in snow presence. Besides climate model differences, a key distinction between our studies is that the analysis here addressed all FGPs but only for AMF, while Musselman et al (2018) investigated ROS events only but for all event magnitudes. Changes in smaller magnitude ROS events are not captured by our analyses, which focus on the mechanisms behind the largest events.

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As noted in the Introduction, the choice of hydrologic model can substantially impact model results. Figure 3 shows the proportion of AMFs triggered by each of the three major FGPs for each of the three hydro-climatic regions and separated by hydrological models. The control and future (RCP 8.5) periods are shown with open circles and filled triangles, respectively. Simulations from every GCM-hydrologic model-headwater basin permutation are then plotted on the ternary graphs. The results from the four different models are shown in different colors, with the marginal distributions of control (dashed lines) and future (solid lines) displayed along the axes of the ternary diagrams. The mean behavior of the four different hydrologic model results is shown in the differently colored bold circles and triangles with connecting lines.

Generally, the hydrologic models agree on their patterns of shifting from snow-driven to precipitation-driven AMF, as seen by the shift of every circle (control) toward the lower right part of the plot for its corresponding triangle (future) in the mean. Nevertheless, we note several nuances. First, there can be a large spread in the FGP partitioning depending on the region. For example, in the montane region while the means of the different hydrologic models are very tightly clustered, the spreads of the simulations are wide. The transition region shows the greatest hydrologic model diversity, with PRMS behaving markedly differently from the three VIC implementations with more precipitation- and ROS-driven AMFs at the expense of snowmelt events.

We also note that hydrologic models can have a strong impact on the attribution of the process causing the AMF. For example, in every region the PRMS implementation consistently exhibits fewer snowmelt and ROS AMFs in favor of precipitation-driven events. This is a result of a simpler elevation representation in PRMS compared to VIC which allows for high elevation snowfall in regions of complex topography. As another example, the VIC-P1 simulations in the temperate region show more snowmelt-driven AMFs, due to a less flashy hydrograph in those simulations, decreasing the peak of the precipitation events causing the snowmelt peak to be the year's highest event. Broadly, however, the relative behaviors of the models are preserved under climate change: the relative positions of each marker on the ternary plot is consistent in control and future periods. For example, the PRMS simulations show less snow in all regions in the control period and continue to in the future.

The changes in AMF for each FGP and their relationship to changes in climate are plotted in the scatter plots of figure 4. Each row corresponds to a different hydro-climatic region and each column corresponds to a different FGP. Every dot represents, for each (ensemble member-hydrologic model-basin) combination, the mean change in temperature and precipitation along on the x-and y-axes, respectively, between control (1980s) and future (2080s) periods, with dots colored by the corresponding change in AMF magnitude. The mean changes in AMF for each FGP/region combination are shown in the upper left bar chart.

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Grouping all hydro-climatic regions, precipitation-driven AMFs show the largest increases in flow magnitude, ranging between 29% and 36%. Increased AMF magnitude and increases in annual precipitation are connected because precipitation-driven AMFs are the result of fast-response runoff. With increased precipitation, soils become increasingly saturated, promoting fast-response flow so an individual precipitation event will be converted preferentially into runoff. ROS-driven AMFs show the smallest changes in magnitude (between −9 and +10%), likely because smaller snowpacks counteract the influence of elevated soil moisture that increases the magnitude of precipitation-driven AMFs.

Changes in snowmelt-driven AMFs depend highly on the hydroclimatic regime: while in the transition and montane regions we see average increases of 12% in magnitude, in temperate regions, AMFs increase by 33%. We can explain this difference using the same logic of the interpretation of the precipitation-driven AMFs above. The general pattern for a snowmelt-driven event includes a period of elevated soil moisture due to the melting of an upstream snowpack. Then, for any year's AMF to satisfy the classification of snowmelt-driven, elevated streamflow due to snowmelt must follow a (series of) precipitation event(s) or warm period triggering intense melt. Because snow plays a smaller role in the temperate region, the AMFs are more likely caused by precipitation events, and thus the sensitivities of the temperate snowmelt-driven AMFs behave more similarly to the precipitation-driven floods. The snowmelt-driven floods in the transition and montane regions experience less of the influence of elevated precipitation and show smaller average increases in AMF (∼12%).

The contours on the panels of figure 4 reflect the multilinear regression of changes in AMF (${{\Delta }}AMF){\text{ }}$on changes in mean annual temperature (${{\Delta }}T$) and annual precipitation (${{\Delta }}P)$

$$\begin{equation}{{\Delta }}AMF = {\beta _0} + {\beta _1}{{\Delta }}T + {\beta _2}{{\Delta }}P.\end{equation} \tag{ 1 }$$

The coefficients ${\beta _1}$and ${\beta _2}$ for each FGP/region combination (see figure 4 panel titles) represent, respectively, the relative increase in AMF magnitude given a 1 °C increase in annual temperature and a 1% increase in annual precipitation. The contours in each panel represent the linear combination of the response in AMF magnitude over a range of changes in temperature and precipitation from equation (1). As a simple linear regression, equation (1) ignores interactions among predictor variables and non-linearities in the response of AMF to changes in climate, but aids in interpretability of the coefficients ${\beta _1}$ and ${\beta _2}$.

Sensitivities are positive for annual precipitation for all FGPs and regions. The region/FGP combinations with higher changes in AMF magnitude also have higher sensitivities. For example, precipitation-driven events in montane regions (average increase 28%; upper left panel) show a high flow sensitivity (${\beta _2}$) of 1.67 %/% (bottom left panel) compared to the 1.40 %/% sensitivity of ROS-driven AMF in montane regions (which only increase on average by 9%). We note that the temperature sensitivity is more variable across FGPs and regions, with the strongest coefficient being −2.0 %/°C. Because the units of changes in temperature [°C] and precipitation [%] are not directly comparable, we cannot say that one scenario is 'twice as sensitive to changes in precipitation as changes in temperature.' Nevertheless, we can conclude that increases in annual precipitation show a much stronger relationship with increases in AMF than changes in annual temperature. Comparing the slopes of the contours between panels in figure 4 allows us to see whether changes in AMF are more strongly related to temperature for one FGP compared to another, or in one hydroclimate compared to another.

Changes in mean climate explain, at most, 49% of the variance in changes in AMF (as seen by the R² values in figure 4), leaving the majority of the variance unexplained. Still, we are able to derive useful first-order relationships between changes in high flows and changes in mean climate. For example, the sensitivity approach explains ∼40% of the variance in montane regions and changes in snowmelt events. We also find that annual precipitation change is a stronger driver of changes in AMF than changes in annual temperature. For example, the montane region shows almost no temperature sensitivity, potentially because AMFs occur during wet, cold times of the year when ET is low and the changes in temperature are not sufficient to cause a large change in precipitation phase. The two sensitivity regressions with the strongest temperature components (see precipitation-driven events in temperate and transition basins) have among the lowest R² of those studied. The one example of a relationship with a stronger temperature sensitivity and an R² above 0.3 is snowmelt events in temperate basins.