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Northeast Colorado Extreme Rains Interpreted in a Climate Change Context
Abstract
The probability for an extreme five-day September rainfall event over northeast Colorado, as was observed in early September 2013, has likely decreased due to climate change.
... On a global scale, the atmosphere has become warmer and moister over the course of the last century and observations suggest that the extent of this increase in moisture is largely in line with the Clausius-Clapeyron relation of approximately 7% per K [1]. However, the relationship between atmospheric moisture content and heavy precipitation is known to exhibit substantial seasonal and regional dependencies, with atmospheric circulation, vertical stability, and actual moisture availability regularly playing a more important role than the moisture-holding capacity of the atmosphere [2,3]. Addressing the question of attribution must thus be placed in a region-specific context that allows for results to differ from case-to-case [4]. ...
... Combined with sustained heavy precipitation over a week-long period and subsequent flooding this resulted in ten fatalities and property damages currently estimated at almost $4 billion (http://denverpost.com/2015/09/12/twoyears-later-2013-colorado-floods-remain-anightmare-for-some/ [3]. ...
... In a previous study focusing on a class of events closely resembling that of September 2013, Hoerling et al [3] found an increase in the intensity of high fiveday average precipitable water across the Boulder area to an extent that would be expected from a greater atmospheric moisture capacity in a warming climate. As a result of these thermodynamic consequences, an increase in the intensity of heavy precipitation over the region would also be anticipated. ...
Understanding and attributing the characteristics of extreme events that lead to societal impacts is a key challenge in climate science. Detailed analysis of individual case studies is particularly important in assessing how anthropogenic climate change is changing the likelihood of extreme events and their associated risk at relevant spatial scales. Here, we conduct a comprehensive multi-method attribution analysis of the heavy precipitation that led to widespread flooding in Boulder, Colorado in September 2013. We provide clarification on the source regions of moisture associated with this event in order to highlight the difficulty of separating dynamic and thermodynamic contributions. Using extreme value analysis of, first of all, historical observations, we then assess the influence of anthropogenic climate change on the overall likelihood of one- and five-day precipitation events across the Boulder area. The same analysis is extended to the output of two general circulation model ensembles. By combining the results of different methods we deduce an increase in the likelihood of extreme one-day precipitation but of a smaller magnitude than what would be expected in a warming world according to the Clausius–Clapeyron relation. For five-day extremes, we are unable to detect a change in likelihood. Our results demonstrate the benefits of a multi-method approach to making robust statements about the anthropogenic influence on changes in the overall likelihood of such an event irrespective of its cause. We note that, in this example, drawing conclusions solely on the basis of thermodynamics would have overestimated the increase in risk.
... Using the conditioning, storyline framework, Trenberth et al. conclude that the 2013 Boulder flood-as well as "Snowmaggedon" in 2010, Superstorm Sandy in 2012, and Typhoon Haiyan in 2013-had a discernible human component, because warmer conditions increased the amount of rain/snow in the event (although this approach makes no statement about the event frequency; 2015). In contrast, scientists using a probabilistic approach arrived at the conclusion that the Boulder flood was not influenced by human-caused climate change (Hoerling 2014 et al.), in the sense that there was no human influence on the frequency of the Boulder type of event. ...
... These are not scientific concepts, so why were they invoked here? We suggest that at least part of the answer can be understood in terms of the response of Martin Hoerling of the U.S. National Oceanic and Atmospheric Administration, who was responsible for the study on the Boulder Flood of 2013, already discussed, that concluded there was no effect from global warming (Hoerling et al. 2014;NAS 2016 p. 86). ...
The most common approaches to detection and attribution of extreme weather events using FAR or RR (Fraction of Attributable Risk or Risk Ratio) answer a particular form of research question, namely, “What is the probability of a certain class of weather events, given global climate change, relative to a world without?” In a set of recent papers, Kevin Trenberth et al. (2015) and Theodore Shepherd (2016) have argued that this is not always the best tool for analyzing causes, or for communicating with the public about climate events and extremes. Instead, they promote the idea of a “storyline” approach, which ask complementary questions, such as “How much did climate change affect the severity of a given storm?” From the vantage of history and philosophy of science, a proposal to introduce a new approach or to answer different research questions—especially those of public interest—does not appear particularly controversial. However, the proposal proved highly controversial, with the majority of detection and attribution scientists reacting in a very negative and even personal manner. Some suggested the proposed alternatives amount to a weakening of standards, or an abandonment of scientific method. Here, we address the question: Why is this such a controversial proposition? We argue that there is no “right” or “wrong” approach to D&A in any absolute sense, but rather that in different contexts society may have a greater or lesser concern with errors of a particular type. How we view the relative risk of over-estimation vs. under-estimation of harm is context-dependent. [250]
... Froma global perspective and particularly in agro-based developing economies during a couple of decades,several drastic droughts caused a significant decline in agricultural production, deceased livestock and increased the issue of food security (S. Ahmed, 2018;Hoerling et al., 2015;Khan et al., 2020). In Pakistan, over the past decades,several droughts occurred whereas from 1998 to 2002 severe drought was experienced which severely reduced water resources and subsequently influenced the food supply and increased food security issues (N. ...
Climate-based natural disaster intensity and destruction severity have risen multifold particularly in developing countries owing to an inadequate understanding of disaster risk perception. In using the household data of 398 respondents and the ordered probit model this study focused to investigate the factors influencing flood, drought, and earthquake risk perception in Punjab, Pakistan. In selected study areas, the majority of respondent’s likelihood perceived of happening disasters which caused financial losses and severely influenced their livelihood. Disasters-prone-area inhabitants’ inadequate understanding of mitigation measures has increased their vulnerability as they have become ineffective to overcome such natural disasters' severe impacts. Empirical estimates of the study indicated as sources of income generating, education level, gender, status, and age of household significantly affect the respondent's risk perception about flood, drought, and earthquake. Females in contrast to male respondents have a limited understanding of mitigation measures and are not as much of capable of controlling disasters as they have become more vulnerable related to disaster effects. In developing the disaster-prone communities' socioeconomic status urgent-based measures are required such as the understanding of the disaster gender-based gap needs to be reduced through better household appreciative vigilance and alleviation of floods, drought, and earthquake disasters.
... For example, localized events and trends sometimes do not accord with more global trends or likelihoods. As a case in point, local extreme storms can be exceedingly unlikely relative to the global regime and yet still occur (e.g., Hoerling et al., 2014;Trenberth et al., 2015). ...
In previous works, I examine inferential methods employed in Probabilistic Weather Event Attribution studies (PEAs), and explored various ways they can be used to aid in climate policy decisions and decision-making about climate justice issues. This paper evaluates limitations of PEAs and considers how PEA researchers’ attributions of “liability” to specific countries for specific extreme weather events could be made more ethical. In sum, I show that it is routinely presupposed that PEA methods are not prone to inductive risks and presuppose that PEA researchers thus have no epistemic consequences or responsibilities for their attributions of liability. I argue that although PEAs are nevertheless crucially useful for practical decision-making, the attributions of liability made by PEA researchers are in fact prone to indicative risks and are influenced by non-epistemic values that PEA researchers should make transparent to make such studies more ethical. Finally, I outline possible normative approaches for making sciences, including PEAs, more ethical; and discuss implications of my arguments for the ongoing debate about how PEAs should guide climate policy and relevant legal decisions.
... It is important to note that despite the seemingly anomalous nature of the September 2013 storm, early fall severe floods have been observed elsewhere in the Front Range (e.g., September 1938 event; Hoerling et al., 2014). The existence of a late-summer through early fall (August-September) flood season can be seen when flood peak seasonality of all USGS stream gages within the SST are examined (Figure 7). ...
Estimating the probabilities of rare floods in mountainous watersheds is challenging due to the hydrometeorological complexity of seasonally‐varying snowmelt and soil moisture dynamics as well as spatiotemporal variability in extreme precipitation. Design storm methods and statistical flood frequency analyses often overlook these complexities and how they shape the probabilities of rare floods. This study presents a process‐based approach that combines gridded precipitation, stochastic storm transposition (SST), and physics‐based distributed rainfall‐runoff modeling to simulate flood peak and volume distributions up to the 10,000‐year recurrence interval and to provide insights into the hydrometeorological drivers of those events. The approach is applied to a small mountainous watershed in the Colorado Front Range in the United States. We show that storm transposition in the Front Range can be justified under existing definitions of regional precipitation homogeneity. The process‐based results show close agreement with a statistically based mixture distribution that considers underlying flood drivers. We further demonstrate that antecedent conditions and snowmelt drive frequent peak discharges and rarer flood volumes, while the upper tail of the flood peak distribution appears to be controlled by heavy rainfall and rain‐on‐snow. In particular, we highlight the important role of early‐fall extreme rainfall in controlling rare flood peaks (but not volumes), despite only one such event having been observed in recent decades. Notwithstanding issues related to the accuracy of gridded precipitation datasets, these findings highlight the potential of SST and process‐based modeling to help understand the relationships between flood drivers and flood frequencies.
... e NAO is a complex nonlinear process with unexplored influence factors, which are difficult to represent in kinetic equations. Besides the discovered factors, such as the geomagnetic activity intensity [4], stratospheric events [5], and sea surface temperature (SST) forcing [6], human influence is also detected as one of the causes of NAO events occurrence [7]. ese uncertainties, along with the uncovered mechanism, make the reliable prediction hard to accomplish. ...
The North Atlantic Oscillation (NAO), which manifests as an irregular atmospheric fluctuation, has a profound effect on the global climate change. The NAO index (NAOI) is the quantitative indicator that can reflect the intensity of the NAO events, and its traditional definition is the normalized sea level pressure (SLP) difference between Azores and Iceland. From the variation tendency of the NAOI, we found that it is difficult to predict the NAO with the characteristics of variability and complexity. As a data-driven approach, the deep neural network presents great potential in learning the mechanisms of climate forecasting. In this paper, we adopt long short-term memory (LSTM) and ConvLSTM to predict the NAO from two aspects, NAOI and SLP, respectively. In previous studies, LSTM has been regarded as a resultful method for time series prediction. ConvLSTM can capture both the temporal and spatial interdependencies of the SLP field; then, the NAOI can be calculated from the SLP output. In order to improve the prediction reliability, we utilize the discrete wavelet transform (DWT) as a preprocessing technique to decompose original data into different frequencies, considering the local time dependency. It can effectively preserve the features of high-frequency data and forecast extreme events more accurately. The proposed DWT-LSTM and DWT-ConvLSTM models are compared against multiple advanced models, such as LSTM, Holt-Winters, support vector regression (SVR), and gated recurrent unit (GRU). The results indicate that both DWT-LSTM and DWT-ConvLSTM perform better, particularly at peak values. As for the 31 NAO events from 2006 to 2015, our models achieve the lowest prediction error and the best stability. Compared with the forecast products of CPC named Global Forecast System (GFS) and the ensemble forecasts (ENSM), our models are much closer to observation in multistep forecasting.
... He and his colleagues concluded there was no effect from global warming. If anything, they said, climate change may have made the Boulder event less likely (Hoerling et al., 2015). Trenberth et al.(2015), on the hand, concluded that human effects did have an impact on the storm results. ...
We start by reviewing the complicated situation in methods of scientific attribution of climate change to extreme weather events. We emphasize the social values involved in using both so-called ``storyline'' and ordinary probabilistic or ``risk-based'' methods, noting that one important virtue claimed by the storyline approach is that it features a reduction in false negative results, which has much social and ethical merit, according to its advocates. This merit is critiqued by the probabilistic, risk-based, opponents, who claim the high ground; the usual probabilistic approach is claimed to be more objective and more ``scientific'', under the grounds that it reduces false positive error. We examine this mostly-implicit debate about error, which apparently mirrors the old Jeffrey-Rudner debate. We also argue that there is an overlooked component to the role of values in science: that of second-order inductive risk, and that it makes the relative role of values in the two methods different from what it first appears to be. In fact, neither method helps us to escape social values, and be more scientifically ``objective'' in the sense of being removed or detached from human values and interests. The probabilistic approach does not succeed in doing so, contrary to the claims of its proponents. This is important to understand, because neither method is, fundamentally, a successful strategy for climate scientists to avoid making value judgments.
... Most of the deluge occurred in a 36-h period, caused extensive property damage and loss of life, and created runoff levels in montane streams across this region with estimated recurrence intervals of 1/50-1/500 years (Gochis et al. 2015). The role of anthropogenic climate change in generating this particular event is debated (Hoerling et al. 2014;Trenberth et al. 2015), but it provided an excellent opportunity to gain insights into biological responses to a rare rainfall extreme. ...
The ecological and evolutionary consequences of extreme events are poorly understood. Here, we tested predictions about species persistence and population genomic change in aquatic insects in 14 Colorado mountain streams across a hydrological disturbance gradient caused by a one in 500-year rainfall event. Taxa persistence ranged from 39 to 77% across sites and declined with increasing disturbance in relation to species' resistance and resilience traits. For taxa with mobile larvae and terrestrial adult stages present at the time of the flood, average persistence was 84% compared to 25% for immobile taxa that lacked terrestrial adults. For two of six species analysed, genomic diversity (allelic richness) declined after the event. For one species it greatly expanded, suggesting resilience via re-colonisation from upstream populations. Thus, while resistance and resilience traits can explain species persistence to extreme disturbance, population genomic change varies among species, challenging generalisations about evolutionary responses to extreme events at landscape scales.
... Kunkel et al. (2013) found an increasing trend in atmospheric PW quantities associated with extreme precipitation events and suggested this trend could lead to an increase in storm intensity. While Hoerling et al. (2014) noted that the September 2013 event was probably not connected to climate change, they did find that heavy precipitation events are becoming more frequent and Karl and Trenberth (2003) found evidence that the number of heavy precipitation events is expected to increase with increasing global temperatures, such as we are experiencing now. The observed and projected increase in the number of heavy precipitation events, combined with the uncertainty of how PW contributes to characteristics of these events, motivated an investigation of PW characteristics surrounding the 2013 event so as to better understand the contributions of PW to an extreme precipitation event with the objective to someday apply these results to future research incorporating a wider variety of events. ...
During 9–16 September 2013, the Front Range region of Colorado experienced
heavy rainfall that resulted in severe flooding. Precipitation totals for the
event exceeded 450 mm, damages to public and private properties were
estimated to be over USD 2 billion, and nine lives were lost. This study
analyzes the characteristics of precipitable water (PW) surrounding the event
using 10 years of high-resolution GPS PW data in Boulder, Colorado, which was
located within the region of maximum rainfall. PW in Boulder is dominated by
seasonal variability with an average summertime maximum of 36 mm. In
2013, the seasonal PW maximum extended into early September and the September
monthly mean PW exceeded the 99th percentile of climatology with a value
25 % higher than the 40-year climatology. Prior to the flood, around
18:00 UTC on 8 September, PW rapidly increased from 22 to 32 mm and
remained around 30 mm for the entire event as a result of the nearly
saturated atmosphere. The frequency distribution of September PW for Boulder
is typically normal, but in 2013 the distribution was bimodal due to a
combination of above-average PW values from 1 to 15 September and much drier
conditions from 16 to 30 September. The above-normal, near-saturation PW
values during the flood were the result of large-scale moisture transport
into Colorado from the Tropical Eastern Pacific and the Gulf of Mexico. This
moisture transport was the product of a stagnating cutoff low over the
southwestern United States working in conjunction with an anticyclone located
over the southeastern United States. A blocking ridge located over the
Canadian Rocky Mountains kept both of the synoptic features in place over the
course of several days, which helped to provide continuous moisture to the
storm, thus enhancing the accumulated precipitation totals.
... Forecasts of future climate could be improved by considering the seasonal feedbacks that these ecosystems will have on both the water and carbon cycles. half of that year's total precipitation fell in this one week (Gochis et al., 2015;Hoerling et al., 2014). Due to the rarity of events such as this, little is known about how ecosystems are impacted by rainfall that is comparable in duration and size to the events that occurred in Colorado that year. ...
Regional climate models project that precipitation in the Great Plains of North America will become characterized by more intense rainfall events separated by longer dry periods. Changing seasonal precipitation patterns may differentially favor grassland productivity in ecosystems dominated by either cool or warm season grass species, and thus influence carbon uptake and loss in these systems. Furthermore, model estimates of ecosystem respiration based primarily on soil temperature could overestimate respiration by failing to account for the effects from saturated conditions during heavy precipitation events. This research contrasted water and carbon fluxes during two years with different intra-annual precipitation within a cool season mixed grassland and compared to a neighboring warm season grassland in Rocky Flats National Wildlife Refuge, Colorado, USA. Results from this study showed a significant positive relationship between the accumulated April/May precipitation and growing season carbon uptake in the cool season, smooth brome-dominated grassland. In addition, significant rainfall in the autumn of 2013 played a role in the early spring growth and carbon uptake in 2014. Comparisons between eddy covariance and soil flux-gradient observations and model estimates of soil respiration showed that during the extreme precipitation event in September 2013, processed-based models better characterized fluxes as compared to empirical models based on soil temperature. The study also found that the cool season grassland was a net sink of carbon during the spring and autumn whereas the neighboring warm season tallgrass prairie was a net sink during the summer. In addition, the study found that the grasslands had considerably different sensitivities to water limitations, with grasses in the tallgrass prairie having a higher water use efficiency (WUE). The comparison of the adjacent semiarid grasslands at Rocky Flats NWR improves our understanding of the response to changing precipitation between cool season and warm season dominated grasslands. This research underscores the importance of expanding grassland research to understand how the composition of grasses will influence carbon cycling, especially as precipitation patterns shift with changing climate. Moreover, this research will add to observations during extreme precipitation events, which can improve both empirical and process-based models of soil respiration.
... Mahoney et al. (2014) analyzed the climatology of rainfall extremes (including snow, hail and graupel) across the Front Range, finding that while the event was similar in character to those associated with the North American Monsoon, it was later than the usual June-August timing. An event with very similar spatial extent occurred in the locality in September 1938 (Boulder Area Sustainability Information Network (BASIN), 2008; Lukas et al., 2014) and multiple climate simulations to recreate September 2013's conditions generated a few more intense cases (Hoerling et al., 2014), highlighting that this event may not be as rare as suggested. However, as noted by Mahoney et al. (2014), the lower density of observation records in population sparse areas and intermittent gauge readings prior to station automation mean that not all historic extremes are captured in the data record and thus cannot be included in a data-based analysis. ...
Between 9th and 16th September 2013, northeast Colorado received some of its most extreme rainfall on record. The event affected 6 major rivers and their tributaries and 14 counties, breaking observed records for accumulations from sub-daily through to annual total. NOAA's rainfall atlases indicated that this event had an anticipated return period of 1000 years. We use the rainfall that led to the 2013 Colorado floods as a case study in order to explore how a large event can affect the generalized extreme value (GEV) parameter estimates often used by designers and planners. We employ daily rainfall observations, with at least 30 years of data, from stations across Colorado's Front Range of the Rocky Mountains to develop a spatial statistical model for annual maximum daily rainfall. We produce estimates of relatively rare events such as the 1% Annual Exceedance Probability (AEP) level and of extremely rare events such as the return period associated with Boulder's 2013 observation. To explore sensitivity, we compare estimates including and excluding data from 2013, and both using only individual station data and our model which borrows strength across multiple stations. We compute the uncertainty associated with all of our estimates, and find large uncertainties associated with extremely rare events. Our statistical model is a spatial hierarchical model and we employ a two-stage approach for inference which can be implemented by practitioners. Additionally, the spatial model allows us to interpolate spatially and estimate the GEV parameters at unobserved locations. A further development of the model makes use of an alternatively defined space in terms of elevation and a climate variable, rather than geographical space defined by longitude and latitude, which seems to better account for orographic effects. In addition to producing AEP level and return period estimates to the annual maximum data, we investigate sensitivity to the choice of block length. We find point estimates indicate the tail to be much heavier when a longer block length is used, but the uncertainty associated with this parameter is such that one cannot say the difference is significant. To describe the spatial extent of severe storms, we also investigate the amount of data dependence between station locations. We find evidence in the record for storms with large spatial extent, although an extremal dependence parameter estimate indicates that this dependence is relatively weak.
Factors responsible for extreme monthly rainfall over Texas and Oklahoma during May 2015 are assessed. The event had a return period of at least 400 years, in contrast to the prior record, which was roughly a 100-yr event. The event challenges attribution science to disentangle factors because it occurred during a strong El Niño, a natural pattern of variability that affects the region’s springtime rains, and during the warmest global mean temperatures since 1880. Effects of each factor are diagnosed, as is the interplay between El Niño dynamics and human-induced climate change.
Analysis of historical climate simulations reveals that El Niño was a necessary condition for monthly rains to occur having the severity of May 2015. The model results herein further reveal that a 2015 magnitude event, whether conditioned on El Niño or not, was made neither more intense nor more likely to be due to human-induced climate change over the past century.
The intensity of extreme May rainfall over Texas and Oklahoma , analogous to the 2015 event, increases by roughly 5% by the latter half of the twenty-first century. No material changes occur in either El Niño–related teleconnections or in overall atmospheric dynamics during extreme May rainfall over the twenty-first century. The increased severity of Texas/Oklahoma May rainfall events in the future is principally due to thermodynamic driving, although much less than implied by simple Clausius–Clapeyron scaling arguments given a projected 23% increase in atmospheric precipitable water vapor. Other thermodynamic factors are identified that act in opposition to the increase in atmospheric water vapor, thereby reducing the effectiveness of overall thermodynamic driving of extreme May rainfall changes over Texas and Oklahoma.
1. Annual precipitation has decreased in much of the West, Southwest, and Southeast and increased in most of the Northern and Southern Plains, Midwest, and Northeast. A national average increase of 4% in annual precipitation since 1901 is mostly a result of large increases in the fall season. (Medium confidence) 2. Heavy precipitation events in most parts of the United States have increased in both intensity and frequency since 1901 (high confidence). There are important regional differences in trends, with the largest increases occurring in the northeastern United States (high confidence). In particular, mesoscale convective systems (organized clusters of thunderstorms)—the main mechanism for warm season precipitation in the central part of the United States—have increased in occurrence and precipitation amounts since 1979 (medium confidence). 3. The frequency and intensity of heavy precipitation events are projected to continue to increase over the 21st century (high confidence). Mesoscale convective systems in the central United States, are expected to continue to increase in number and intensity in the future (medium confidence). There are, however, important regional and seasonal differences in projected changes in total precipitation: the northern United States, including Alaska, is projected to receive more precipitation in the winter and spring, and parts of the southwestern United States are projected to receive less precipitation in the winter and spring (medium confidence). 4. Northern Hemisphere spring snow cover extent, North America maximum snow depth, snow water equivalent in the western United States, and extreme snowfall years in the southern and western United States have all declined, while extreme snowfall years in parts of the northern United States have increased (medium confidence). Projections indicate large declines in snowpack in the western United States and shifts to more precipitation falling as rain than snow in the cold season in many parts of the central and eastern United States (high confidence).
Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380
For further resources related to this article, please visit the WIREs website.
The Colorado Front Range has a large elevation gradient with deep seasonal snowpack in the mountains and limited snow accumulation in the foothills and plains. This study examines how the sources of annual peak flows (snowmelt, rainfall, mixed) change with the fraction of time snow persists on the ground, snow persistence (SP), and whether these sources have changed over time. Sources of peak flows for 20 gaging stations are estimated using a gridded rain and snow model forced with PRISM daily precipitation and both PRISM and TopoWx temperature. The mean snowmelt contribution to peak flow is highly correlated with SP (r2=0.86-0.90). Watersheds with SP<0.3 (low snow, elevation <2000 m) are rainfall-dominated, and watersheds with SP>0.7 (persistent snow, elevation >3100 m) are mostly snowmelt-dominated, with mixed sources between these thresholds. Rainfall runoff peak flows are possible at all elevations, but their likelihood declines with increasing SP. Rainfall runoff from an extreme storm in September 2013 produced the highest annual peaks at many stations, including some snowmelt-dominated watersheds. Regional Kendall trend tests indicate that the contributions of snowmelt to peak flows and total annual inputs have declined in the mixed source zone. These changes may affect hydrographs, as analyses confirm that snowmelt runoff generally produces more attenuated peaks than rainfall runoff. Discrimination of peak flow source is sensitive to input data and model structure for mixed rain and snowmelt events, and both observation and modeling research are needed to help understand potential runoff changes in these conditions.
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