Landslide-channel feedbacks amplify channel widening during floods

Landslide-channel feedbacks amplify channel widening during floods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Landslide-channel feedbacks amplify channel widening during floods Georgina L Bennett, Diego Panici, Francis Rengers, Jason Kean, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3937459/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2025 Read the published version in npj Natural Hazards → Version 1 posted 4 You are reading this latest preprint version Abstract Channel widening is a major hazard during floods, particularly in confined mountainous catchments where roads and buildings compete for space with river channels. Flood-induced channel widening is also an important process in eroding and shaping the landscape. However, channel widening during floods is not well understood and not always explained by hydraulic variables alone, with implications for flood risk management. Floods in mountainous regions often coincide with landslides triggered by heavy rainfall on steep valley sides. Whilst the long-term impact of increased sediment supply on channel widening is well established, landslide-channel interactions at the event timescale are not well known or documented. Here we demonstrate with an example from the Great Colorado Flood in 2013, a 1000-yr precipitation event that induced a 200-yr flood, and 100-yrs of erosion, how landslide-channel feedbacks can substantially amplify channel widening and flood risk. We use a combination of field analysis and multiphase flow modeling to document landslide-channel interaction during the flood event in which sediment delivered by landslides temporarily dammed the channel before failing and bulking the flow with sediment resulting in large channel widening. We propose that such landslide-flood interactions will become increasingly important to account for in flood hazard assessment as flooding and landsliding increase with extreme rainfall under climate change. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Hydrology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Flood stream power is amplified in mountainous catchments by channel confinement and steep slopes, generating widespread channel erosion and causing substantial challenges for flood risk management in these regions. Approaches to predicting flood channel response include identification of stream power thresholds (Magilligan, 1992 ; Yochum et al., 2017 ), duration of flow above a critical value (Costa and O Connor, 1995), and downstream gradients in stream power (Gartner et al., 2015 ). However, other studies have found that hydraulic forces alone are not able to explain geomorphic impacts of floods (e.g., Heritage et al., 2004 ; Nardi and Rinaldi, 2015 ; Surian et al., 2016 ) and highlight the importance of other factors such as human obstructions (Langhammer, 2010 ); lateral confinement (Thompson and Croke, 2013 ; Sholtes et al., 2018 ; Ruiz-Villanueva et al., 2023 ); pre-flood channel planform (e.g., Surian et al., 2016 ) and channel bed particle packing geometry and stability (e.g., East et al., 2018 ; Masteller et al., 2019 ). Floods in mountainous catchments often coincide with rainfall-induced landslides on steep valley walls that may deliver substantial volumes of sediment into flooded channels (e.g., Rathburn et al., 2017 , Abanco et al., 2021 ) and downstream receiving waters (Eidmann et al., 2022 ). Landslides may interact with and influence channel processes in several ways. First, sediment delivered by landslides may bulk the flow, increasing its bulk density and therefore power, explaining why debris flows and debris floods (Church and Jakob, 2020 ) are more erosive than floods (e.g., Brenna et al., 2023 ). Landslides may dam channels, partially or completely blocking channel flow for some period, before potentially bursting and causing a powerful and erosive flood downstream (Costa and Schuster, 1987 ; Korup, 2005 ; Korup et al., 2010 ; Ruiz-Villanueva et al., 2017 ). Over the long-term, lateral sediment supply from debris flows, landslides, and other sources is known to lead to channel widening due to heightened bed material flux in response to sudden sediment input, or passage of sediment through the channel system as a slug (Miller and Benda, 2000 ; Cui et al, 2003 , Hoffman and Gabet, 2007 ; Nelson and Dube, 2016; Baynes et al., 2020 ; Rachelly et al., 2022 ). However, observations of landslide-channel interactions and their influence on morpho and hydrodynamics are rare, partly due to a lack of evidence left behind in the channel after a flood and the difficulty of observing channel dynamics during a flood. Given that extreme rainfall and associated landslides and flooding are increasing with climate change (Gariano & Guzzetti, 2016 ; East et al., 2022 ), such interactions and geomorphic impacts are increasingly important to understand. Here we use high resolution light detection and ranging (lidar) pre- and post-flood digital elevation models (DEMs) together with detailed field analysis and numerical modeling to reconstruct landslide-channel interactions and flood dynamics in a catchment affected by an extreme flood and landsliding. Study area and extreme flood and landsliding event In September 2013, the Colorado Front Range, USA, experienced a 1000-year rainfall event (Gochis et al., 2015 ). Between 9–15 September 2013 the storm dropped up to 500 mm of rain, or 10 times the average September rainfall, in this semi-arid region. The resultant flood, estimated to have a 200-yr recurrence interval (Yochum et al., 2017 ), killed 8 people and caused extensive damage to roads and infrastructure in catchments across the Front Range (e.g., Fig. 1 a). The storm also triggered > 1000 shallow landslides and debris flows (Coe et al., 2014 ). The North Saint Vrain catchment (Fig. 1 b) received some of the greatest rainfall of any catchment across the range with multiple landslides triggered. The main flood occurred on 11 September once soils were saturated and had an estimated peak discharge at the outlet above Apple Valley Bridge, Lyons, Colorado, of 385 ± 80 m 3 /s (Moody, 2016 ). The mean annual peak flow is 20 m 3 /s (Wohl et al., 2004 ). Rathburn et al. ( 2017 ) produced a detailed flood sediment budget based on differencing of pre- and post-flood lidar and reservoir coring (pre-flood lidar collected April-October 2011 and post-flood lidar collected October 2013 and July 2014). They found that the flood caused ~ 100 years of erosion and reservoir sedimentation, with half of the erosion occurring through landsliding and with the majority of channel erosion (sediment not bedrock) occurring via channel widening rather than vertical incision (Rathburn et al., 2017 ). This study also indicated a possible role of large wood in avulsions and channel widening associated with the large volume of wood stripped from hillslopes and channel banks. In addition, floodplain disturbance resulting from the 2013 flood coincided with areas of the valley that were less confined (Sutfin and Wohl, 2019 ). However, further questions remain regarding the controls on the variability in channel widening observed along the channel such as the role of landslide-channel coupling and related flood dynamics. The upper North Saint Vrain catchment (upstream from the Ralph Price Reservoir) is a good setting in which to investigate the controls on channel widening as it is one of the few catchments along the Front Range with no human development or obstructions to interrupt hillslope-channel coupling and flood response. Here we investigate the sudden and substantial increase in channel widening and erosion (Figs. 1 c and 2 ) that occurred 7 km downstream from the start of the study channel (due east of Route 7 in Fig. 1 b). The mapping of pre- and post-flood channel widths and valley floor width and calculation of channel widening and erosion is detailed in Supplementary Information. The first 7 km of the study channel reach had little channel erosion (~ 1.4 m 3 /m) and an average widening rate of 5 m or just half of the average preflood channel width (9.94 m), as estimated from the lidar DEM of difference (DoD) (Rathburn et al., 2017 ). The valley floor remained undisturbed at several points (also refer to Sutfin and Wohl, 2019 ). Downstream from this point, channel erosion (~ 20.7 m 3 /m) was much greater and had an average widening of 28 m (3 times preflood channel width). The flood almost fully occupied the valley floor and so widening was achieved mostly through removal of valley floor sediment, although with some evidence of bedrock erosion on outer bends of the channel (e.g., Figure S5). The most rapid transition occurs at 7 km downstream over a roughly 1.5-km reach, where channel widening reaches 67 m and erosion averages 38 m 3 /m. This transition is not explained by peak flood stream power (Fig. 2 ), as estimated from radar-based rainfall data (Methods), which increases gradually downstream but does not provide the trigger of the sudden 6 times increase in channel widening. Whilst valley confinement, calculated as the ratio of valley width to pre flood channel width, constrains channel widening beyond 7 km downstream (Figure S3), channel widening upstream from this point is lacking despite relatively unconstrained channels (i.e., confinement ratio > = 3, Sholtes et al., 2018 ) along the entirety of the study channel. However, the sudden increase in channel erosion and widening does occur just downstream from three major lateral sediment inputs from two landslides (L1, L2 in Fig. 1 c) and a tributary, totaling ~ 105,000 m 3 . We used a combination of field data and numerical modeling to investigate the potential mechanisms by which landslide-channel interactions may have resulted in the pattern of channel erosion observed. We tested the hypotheses that the pattern of channel erosion generated by the flood was amplified by (1) bulking of the flow by landslide and tributary sediment; (2) damming of the flow by landslide and tributary sediment, and subsequent failure of this dam resulting in an erosive flood surge similar to that observed in debris flows (Kean et al., 2016 ). Field evidence of landslide-flood interaction As an initial test for sediment bulking or surging of the flow that may explain the sudden increase in channel erosion and widening, we calculated a ratio of field-measured to runoff-based peak discharge, C (e.g., Kean et al., 2016 ). Flood flows can have a range of C values between 0 and 1 (e.g., Lapides et al., 2021 ). Due to conservation of mass, this ratio cannot exceed 1 in the absence of substantial sediment bulking or surge dynamics and can be used as a check on indirect measurements of flood discharge (e.g., Moody, 2016 ). A value of C > 1 is diagnostic of bulked and/or surging in the flow that would provide initial support for hypothesis 2 of a landslide dam failure. We visited the reach of increased channel erosion in October 2016 and August 2017 to make indirect measurements of flood peak discharge (Q peak ) with which to compare our estimated runoff-based discharge (Q runoff ) (Methods) and calculate the ratio C. We used a differential Global Positioning System (GPS) to collect pairs of highwater marks at 23 channel cross sections upstream and downstream from the transition reach (Fig. 1 c) in 2016 and 2017 with a vertical and horizontal precision of 0.6 m and 0.4 m, respectively. We identified highwater marks based on debris lines on the channel banks and debris trapped in trees. We used the highwater marks within ArcGIS (Esri, Redlands, California) to extract cross sections from lidar data. Where there is deviation in highwater mark elevations between the left and right banks, we propagate this uncertainty into our estimation of peak discharge cross-section area (Figure S6 and S7). Additionally, we calculated cross-section areas using both pre- and post-flood lidar to account for uncertain channel topography during the flood. We calculated peak discharge as $${Q}_{peak}=VA$$ where A is cross-section area and V is velocity. Velocity was calculated using the critical flow method as: $$V=\sqrt{gR}$$ where R is hydraulic radius calculated as \(R=\frac{A}{P}\) , where P is wetted perimeter and assuming critical flow (i.e., Froude number = 1). The critical flow method has been shown by Moody ( 2016 ) to give values most representative of the average flow conditions during the flood from an ensemble of peak discharge estimation techniques, potentially due to smoothing of the channel bed by sediment infilling during the flood. Although Q runoff only slightly increases along the transition reach from about 217 to 218 m 3 /s, Q peak shows a rapid increase from 72 m 3 /s (± 5 m 3 /s) to 468 m 3 /s (± 190 m 3 /s) over the ~ 300 m downstream from L2 (Fig. 3 c). Therefore, the estimated runoff coefficient, C, also increases from 0.33 (± 0.02) to 2.15 (± 0.87), with values > 1 diagnostic of surge type behavior and/or sediment bulking as can occur in debris flows (Fig. 3 c) (Kean et al., 2016 . Downstream from the L2 landslide entry, a cobble and boulder rich bar is clearly visible in satellite photos (Figure S1 ) and was found to have a median grain size of 22 cm (Figure S4). The post-flood channel profile also shows this wedge of sediment (Fig. 3 b). We used numerical modelling, detailed below, to test the hypothesis that this bar is what remains of a dam that was formed and then rapidly removed during the flood. The precise timing of landslides that could have caused a valley damming event is uncertain but previous modeling of landslides in the catchment (including L2) indicates landslides occurred shortly after storm rainfall peaked late in the evening on September 11, with landslides on south facing slopes (including L2) occurring late in the evening of September 11 and landslides on north facing slopes occurring in the early morning of September 12 (McGuire et al., 2016 ). Witness accounts also record the first landslides happening at this time in neighboring catchments (Coe et al., 2014 ; Ebel et al., 2015 ). The ratio C calculated based on our field observations supports the dam removal hypothesis because the rapid jump in widening at a distinct channel location indicates that a surge of sediment and water similar to a debris flow would be needed to generate erosional widening over a short distance. A dam break could explain the bank erosion by bulking the flow and generating a positive feedback on bank erosion and channel widening. This has been observed for other dam burst events (Costa and Schuster, 1987 ), and therefore the dam break hypothesis could cause the observed profound channel widening of up to 67 m. Multi-phase modeling of landslide-flood interaction To further test the hypothesis of a dam formation and burst event, we carried out numerical simulations with the computational model r.avaflow (v. 2.4) (Pudasaini and Mergili, 2019 ) to simulate the sediment delivery to the valley floor and its interactions with the fluid flow of North Saint Vrain Creek. The computational model r.avaflow is a freely available deterministic, multi-phase model, which is based on the principles of energy and momentum transfer between liquid and solid phases, extending the use of a Voellmy-type resistance model to multiple phases (Pudasaini and Mergili, 2019 ; Mergili et al., 2020 ). Furthermore, r.avaflow has been used to simulate a range of hydro-geomorphic process chains from rock avalanches (Mergili et al., 2018 ), transition of rock avalanches into debris flows (Shugar et al., 2021 ) to glacial lake outburst floods triggered by landslides into lakes (Vilca et al., 2021 ). For fluvial systems, r.avaflow has been validated for glacial debris flow mobility in mountainous river channels (Wang et al., 2023 ), entrainment of sediment by debris flows in channels (Baggio et al., 2021 ), and channel changes from cascading rockslide-channelized debris flow (Mergili et al., 2020 ). However, to our knowledge it has not before been used to simulate landslide-flood interaction. For the simulation input, we used the landslides mapped from the DoD for the solid phase and the discharge Q runoff for the fluid phase. Because the total simulation time is relatively small (900 seconds in total), the fluid hydrograph has been taken as a constant discharge, corresponding to the peak flow Q runoff . Because the landslides were mostly formed by coarse sediment (based on in situ observations after the event), the input sediment phase was considered only as coarse (i.e., friction-dominated for the model computations), whereas no input has been used for the fine phase (i.e., inclusive of viscous effects). Although large wood likely played a role in channel widening in this event (Rathburn et al., 2017 ), r.avaflow currently does not include this component, but this could be considered in future versions of the model, particularly in the case of hyper-congested flows (Ruiz-Villanueva et al., 2019 ). The simulation was performed in two stages due to a limitation of the model in the version used (2.4), where topography is not updated during the model simulation: (1) dam formation is simulated related to triggering of landslides L1 and L2 and delivery of sediment to the channel and topography is updated to include the dam, and (2) dam removal is simulated using updated topography from stage (1). Boundary conditions and flow hydrographs remain the same for both steps. In the model, landslides have been assumed to be released simultaneously. This had minor effects on the simulations because the delivery time to the valley bottom was different for each individual landslide, and all occurred within 30–45 seconds from the start of the simulation. Several sets of simulations have been conducted to explore the parameter space, including mobility and erosive parameters, as well as the hydrographs of the main channel and tributaries (Table S2). Because all parameters are physically based, we used values that are conventionally found in the literature or recommended by the r.avaflow manual (Mergili 2014–2020) depending on the type of event. Figure 4 shows the topographic change within the study area at the end of the simulations for both dam formation (first stage) (Fig. 4 a) and dam removal (second stage) (Fig. 4 b) superimposed on the observed post-event channel extent. Irrespective of the parameters used for the simulations, the model supports the formation of a large sediment dam in the channel downstream from L2, as well as another smaller dam upstream related to L1 (Fig. 4 a). Furthermore, when the topography of the area is updated with the deposition (or erosion) heights from the first simulation stage, the second stage model runs (Fig. 4 b) show that substantial erosion occurs, removing the large dam within the simulation time of 600 seconds. The bulk of the dam is eroded over a period of approximately 300 seconds and leads to the erosion and widening of the channel downstream, due to bulking of the flow by sediment. The final simulated topographic change (Fig. 4 c) corresponds well with the observed pattern of erosion from the DoD shown in Figs. 1 c and 2 . There are some discrepancies between the simulation and observations. For example, the simulated dam occurs closer to the toe of the landslide L2 than the remnants of the dam we observed in the field, which occur ~ 100 m downstream (Fig. 3 ). However, these are to be expected considering challenges in replicating spatially explicit two- and three-dimensional landscape evolution in fluvial morphodynamic models (e.g., Lauer et al., 2016 ) To further test the hypothesis that the topographic change of the channel was caused by the formation and then removal of the sediment dam, we performed an additional simulation (Fig. 4 d) without landslide sediment input, to evaluate whether the flood flow alone and the sediment it mobilized from upstream to downstream (i.e., not including landslide sediment input) could have caused the channel change. In this scenario, only limited erosion (i.e., < 0.10 m) was observed in the main channel downstream from L2 and the extent was substantially smaller than the post-event observations. This is in contrast with the simulations inclusive of landslide input (Fig. 4 c), in which the post-event channel extents were much more consistent with the observed changes. We therefore conclude that a landslide-dam break and rapid delivery of sediment best explains the observed geomorphic channel change during the flood. All the numerical simulations involving landslides qualitatively agree on the formation and removal of a dam, whereby the downstream channel widening resulted from bulking of the flow by sediment related to a removal of the debris dam. Landslide-flood interaction amplified flood channel widening Previous studies have documented cycles of hillslope-channel coupling over multi-event timescales with hillslopes delivering sediment to the channel during a rainstorm, for example, and subsequent floods clearing this from the channel system (e.g., Harvey et al., 2001; Berger et al., 2011 ; Bennett et al., 2014 ). Other studies have documented the occurrence of landslide-dams and the erosive power of dam burst floods, whether landslide induced or otherwise (Costa and Schuster, 1987 , Jarrett and Costa, 1986). Here, we have documented with a combination of detailed field data analysis and numerical modeling how landslide-delivered sediment may interact with the main channel during an individual flood through formation and failure of a dam and bulking of the flow by sediment. In agreement with prior studies, we observed that landslides delivering sediment into a fluvial channel led to channel widening (Nelson and Dube, 2016; Cui et al, 2003 , Hoffman and Gabet, 2007 ; Miller and Benda, 2000 ; Baynes et al., 2020 ; Rachelly et al., 2022 ). Here we outline that transition with the following stages. In Stage 1, landslides triggered by heavy rainfall deliver sediment to the flood flow and temporarily dam the channel. In Stage 2, the flood starts to gradually remove the dam, bulking the flow with sediment and causing it to erode channel banks downstream. In Stage 3, the flow continues to be loaded with sediment from the dam and from bank erosion causing further erosion of outer banks, although remnants of the dam remain. Although it is likely that large wood played a role in the pattern of channel widening (Rathburn et al., 2017 ) as observed in other catchments, modeling indicates that sediment delivered by landslides to the flood flow is crucial for explaining the substantial channel widening observed in this event (Fig. 4 c,d). We have focused here on the damming and erosion related to the larger landslide L2, although we note that farther upstream landslide L1 also forms a small dam at its toe in the model and probably explains the channel widening of the opposite bank of the river at that location (Fig. 4 b). We have focused on landslide sediment delivery directly into the study channel but sediment input from tributaries may result in similar effects. For example, at ~ 13 km downstream a tributary delivered ~ 5 x 10 4 m 3 of sediment (predominantly from landslides along the tributary) potentially damming and/or bulking the flow and resulting in the peak in channel widening just downstream. We suspect that similar landslide-channel interactions may be responsible for higher than expected peak discharge relative to rainfall runoff in other Colorado Front Range catchments flooded in 2013 (Moody, 2016 ). Furthermore, such landslide-channel interactions may be relevant for understanding and predicting channel widening and erosion during floods in other mountainous regions of the world. Given that extreme rainfall and associated landslides and flooding are expected to increase in a warmer climate (East et al., 2022 ), and that extreme rainfall can be more important in generating landslides than earthquakes, even in places that are tectonically active (Jones et al., 2022; LaHusen et al., 2020 ; Odin et al., 2019), such landslide-flood interactions will be increasingly important to account for. Field evidence of landslide-flood interaction As an initial test for sediment bulking or surging of the flow that may explain the sudden increase in channel erosion and widening, we calculated a ratio of field-measured to runoff-based peak discharge, C (e.g., Kean et al., 2016 ). Flood flows can have a range of C values between 0 and 1 (e.g., Lapides et al., 2021 ). Due to conservation of mass, this ratio cannot exceed 1 in the absence of substantial sediment bulking or surge dynamics and can be used as a check on indirect measurements of flood discharge (e.g., Moody, 2016 ). A value of C > 1 is diagnostic of bulked and/or surging in the flow that would provide initial support for hypothesis 2 of a landslide dam failure. We visited the reach of increased channel erosion in October 2016 and August 2017 to make indirect measurements of flood peak discharge (Q peak ) with which to compare our estimated runoff-based discharge (Q runoff ) (Methods) and calculate the ratio C. We used a differential Global Positioning System (GPS) to collect pairs of highwater marks at 23 channel cross sections upstream and downstream from the transition reach (Fig. 1 c) in 2016 and 2017 with a vertical and horizontal precision of 0.6 m and 0.4 m, respectively. We identified highwater marks based on debris lines on the channel banks and debris trapped in trees. We used the highwater marks within ArcGIS (Esri, Redlands, California) to extract cross sections from lidar data. Where there is deviation in highwater mark elevations between the left and right banks, we propagate this uncertainty into our estimation of peak discharge cross-section area (Figure S6 and S7). Additionally, we calculated cross-section areas using both pre- and post-flood lidar to account for uncertain channel topography during the flood. We calculated peak discharge as $${Q}_{peak}=VA$$ where A is cross-section area and V is velocity. Velocity was calculated using the critical flow method as: $$V=\sqrt{gR}$$ where R is hydraulic radius calculated as \(R=\frac{A}{P}\) , where P is wetted perimeter and assuming critical flow (i.e., Froude number = 1). The critical flow method has been shown by Moody ( 2016 ) to give values most representative of the average flow conditions during the flood from an ensemble of peak discharge estimation techniques, potentially due to smoothing of the channel bed by sediment infilling during the flood. Although Q runoff only slightly increases along the transition reach from about 217 to 218 m 3 /s, Q peak shows a rapid increase from 72 m 3 /s (± 5 m 3 /s) to 468 m 3 /s (± 190 m 3 /s) over the ~ 300 m downstream from L2 (Fig. 3 c). Therefore, the estimated runoff coefficient, C, also increases from 0.33 (± 0.02) to 2.15 (± 0.87), with values > 1 diagnostic of surge type behavior and/or sediment bulking as can occur in debris flows (Fig. 3 c) (Kean et al., 2016 . Downstream from the L2 landslide entry, a cobble and boulder rich bar is clearly visible in satellite photos (Figure S1 ) and was found to have a median grain size of 22 cm (Figure S4). The post-flood channel profile also shows this wedge of sediment (Fig. 3 b). We used numerical modelling, detailed below, to test the hypothesis that this bar is what remains of a dam that was formed and then rapidly removed during the flood. The precise timing of landslides that could have caused a valley damming event is uncertain but previous modeling of landslides in the catchment (including L2) indicates landslides occurred shortly after storm rainfall peaked late in the evening on September 11, with landslides on south facing slopes (including L2) occurring late in the evening of September 11 and landslides on north facing slopes occurring in the early morning of September 12 (McGuire et al., 2016 ). Witness accounts also record the first landslides happening at this time in neighboring catchments (Coe et al., 2014 ; Ebel et al., 2015 ). The ratio C calculated based on our field observations supports the dam removal hypothesis because the rapid jump in widening at a distinct channel location indicates that a surge of sediment and water similar to a debris flow would be needed to generate erosional widening over a short distance. A dam break could explain the bank erosion by bulking the flow and generating a positive feedback on bank erosion and channel widening. This has been observed for other dam burst events (Costa and Schuster, 1987 ), and therefore the dam break hypothesis could cause the observed profound channel widening of up to 67 m. Multi-phase modeling of landslide-flood interaction To further test the hypothesis of a dam formation and burst event, we carried out numerical simulations with the computational model r.avaflow (v. 2.4) (Pudasaini and Mergili, 2019 ) to simulate the sediment delivery to the valley floor and its interactions with the fluid flow of North Saint Vrain Creek. The computational model r.avaflow is a freely available deterministic, multi-phase model, which is based on the principles of energy and momentum transfer between liquid and solid phases, extending the use of a Voellmy-type resistance model to multiple phases (Pudasaini and Mergili, 2019 ; Mergili et al., 2020 ). Furthermore, r.avaflow has been used to simulate a range of hydro-geomorphic process chains from rock avalanches (Mergili et al., 2018 ), transition of rock avalanches into debris flows (Shugar et al., 2021 ) to glacial lake outburst floods triggered by landslides into lakes (Vilca et al., 2021 ). For fluvial systems, r.avaflow has been validated for glacial debris flow mobility in mountainous river channels (Wang et al., 2023 ), entrainment of sediment by debris flows in channels (Baggio et al., 2021 ), and channel changes from cascading rockslide-channelized debris flow (Mergili et al., 2020 ). However, to our knowledge it has not before been used to simulate landslide-flood interaction. For the simulation input, we used the landslides mapped from the DoD for the solid phase and the discharge Q runoff for the fluid phase. Because the total simulation time is relatively small (900 seconds in total), the fluid hydrograph has been taken as a constant discharge, corresponding to the peak flow Q runoff . Because the landslides were mostly formed by coarse sediment (based on in situ observations after the event), the input sediment phase was considered only as coarse (i.e., friction-dominated for the model computations), whereas no input has been used for the fine phase (i.e., inclusive of viscous effects). Although large wood likely played a role in channel widening in this event (Rathburn et al., 2017 ), r.avaflow currently does not include this component, but this could be considered in future versions of the model, particularly in the case of hyper-congested flows (Ruiz-Villanueva et al., 2019 ). The simulation was performed in two stages due to a limitation of the model in the version used (2.4), where topography is not updated during the model simulation: (1) dam formation is simulated related to triggering of landslides L1 and L2 and delivery of sediment to the channel and topography is updated to include the dam, and (2) dam removal is simulated using updated topography from stage (1). Boundary conditions and flow hydrographs remain the same for both steps. In the model, landslides have been assumed to be released simultaneously. This had minor effects on the simulations because the delivery time to the valley bottom was different for each individual landslide, and all occurred within 30–45 seconds from the start of the simulation. Several sets of simulations have been conducted to explore the parameter space, including mobility and erosive parameters, as well as the hydrographs of the main channel and tributaries (Table S2). Because all parameters are physically based, we used values that are conventionally found in the literature or recommended by the r.avaflow manual (Mergili 2014–2020) depending on the type of event. Figure 4 shows the topographic change within the study area at the end of the simulations for both dam formation (first stage) (Fig. 4 a) and dam removal (second stage) (Fig. 4 b) superimposed on the observed post-event channel extent. Irrespective of the parameters used for the simulations, the model supports the formation of a large sediment dam in the channel downstream from L2, as well as another smaller dam upstream related to L1 (Fig. 4 a). Furthermore, when the topography of the area is updated with the deposition (or erosion) heights from the first simulation stage, the second stage model runs (Fig. 4 b) show that substantial erosion occurs, removing the large dam within the simulation time of 600 seconds. The bulk of the dam is eroded over a period of approximately 300 seconds and leads to the erosion and widening of the channel downstream, due to bulking of the flow by sediment. The final simulated topographic change (Fig. 4 c) corresponds well with the observed pattern of erosion from the DoD shown in Figs. 1 c and 2 . There are some discrepancies between the simulation and observations. For example, the simulated dam occurs closer to the toe of the landslide L2 than the remnants of the dam we observed in the field, which occur ~ 100 m downstream (Fig. 3 ). However, these are to be expected considering challenges in replicating spatially explicit two- and three-dimensional landscape evolution in fluvial morphodynamic models (e.g., Lauer et al., 2016 ) To further test the hypothesis that the topographic change of the channel was caused by the formation and then removal of the sediment dam, we performed an additional simulation (Fig. 4 d) without landslide sediment input, to evaluate whether the flood flow alone and the sediment it mobilized from upstream to downstream (i.e., not including landslide sediment input) could have caused the channel change. In this scenario, only limited erosion (i.e., < 0.10 m) was observed in the main channel downstream from L2 and the extent was substantially smaller than the post-event observations. This is in contrast with the simulations inclusive of landslide input (Fig. 4 c), in which the post-event channel extents were much more consistent with the observed changes. We therefore conclude that a landslide-dam break and rapid delivery of sediment best explains the observed geomorphic channel change during the flood. All the numerical simulations involving landslides qualitatively agree on the formation and removal of a dam, whereby the downstream channel widening resulted from bulking of the flow by sediment related to a removal of the debris dam. Landslide-flood interaction amplified flood channel widening Previous studies have documented cycles of hillslope-channel coupling over multi-event timescales with hillslopes delivering sediment to the channel during a rainstorm, for example, and subsequent floods clearing this from the channel system (e.g., Harvey et al., 2001; Berger et al., 2011 ; Bennett et al., 2014 ). Other studies have documented the occurrence of landslide-dams and the erosive power of dam burst floods, whether landslide induced or otherwise (Costa and Schuster, 1987 , Jarrett and Costa, 1986). Here, we have documented with a combination of detailed field data analysis and numerical modeling how landslide-delivered sediment may interact with the main channel during an individual flood through formation and failure of a dam and bulking of the flow by sediment. In agreement with prior studies, we observed that landslides delivering sediment into a fluvial channel led to channel widening (Nelson and Dube, 2016; Cui et al, 2003 , Hoffman and Gabet, 2007 ; Miller and Benda, 2000 ; Baynes et al., 2020 ; Rachelly et al., 2022 ). Here we outline that transition with the following stages. In Stage 1, landslides triggered by heavy rainfall deliver sediment to the flood flow and temporarily dam the channel. In Stage 2, the flood starts to gradually remove the dam, bulking the flow with sediment and causing it to erode channel banks downstream. In Stage 3, the flow continues to be loaded with sediment from the dam and from bank erosion causing further erosion of outer banks, although remnants of the dam remain. Although it is likely that large wood played a role in the pattern of channel widening (Rathburn et al., 2017 ) as observed in other catchments, modeling indicates that sediment delivered by landslides to the flood flow is crucial for explaining the substantial channel widening observed in this event (Fig. 4 c,d). We have focused here on the damming and erosion related to the larger landslide L2, although we note that farther upstream landslide L1 also forms a small dam at its toe in the model and probably explains the channel widening of the opposite bank of the river at that location (Fig. 4 b). We have focused on landslide sediment delivery directly into the study channel but sediment input from tributaries may result in similar effects. For example, at ~ 13 km downstream a tributary delivered ~ 5 x 10 4 m 3 of sediment (predominantly from landslides along the tributary) potentially damming and/or bulking the flow and resulting in the peak in channel widening just downstream. We suspect that similar landslide-channel interactions may be responsible for higher than expected peak discharge relative to rainfall runoff in other Colorado Front Range catchments flooded in 2013 (Moody, 2016 ). Furthermore, such landslide-channel interactions may be relevant for understanding and predicting channel widening and erosion during floods in other mountainous regions of the world. Given that extreme rainfall and associated landslides and flooding are expected to increase in a warmer climate (East et al., 2022 ), and that extreme rainfall can be more important in generating landslides than earthquakes, even in places that are tectonically active (Jones et al., 2022; LaHusen et al., 2020 ; Odin et al., 2019), such landslide-flood interactions will be increasingly important to account for. Declarations Author Contribution G.B. conceived the study with input from S.R., J.K. and F.R. G.B., J.K., and F.R., conducted fieldwork. G.B. performed analysis of lidar and field data with input from J.K. and F.R. D.P. conducted r.avaflow modeling. G.B. wrote the manuscript with contributions from D.P., F.R., J.K., and S.R. G.B. prepared figures 1 - 3 and D.P. prepared figure 4. All authors reviewed the manuscript. Acknowledgements Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 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Stream power and geomorphic change during the 2013 Colorado Front Range flood. Geomorphology. 292 (2017). https://doi.org/10.1016/j.geomorph.2017.03.004 Additional Declarations No competing interests reported. Supplementary Files FMBAORevisedSIcleanFKRGB.docx Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2025 Read the published version in npj Natural Hazards → Version 1 posted Editorial decision: Revision requested 08 Feb, 2024 Editor assigned by journal 08 Feb, 2024 Submission checks completed at journal 08 Feb, 2024 First submitted to journal 07 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3937459","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271827560,"identity":"17790947-0930-4020-82bd-376ee1b5807f","order_by":0,"name":"Georgina L Bennett","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie2RsUpDMRSGTziQqeWuCR3yCikFQSzqo3gp6CaFLhmkBApxudg1j+EbeMuBO+UBFB0UwbluFUXNhSIi5KKbQ74pHPLlPz8ByGT+JWhxe2L33+c6rbCtwhHbW+JPChe/UtRhVKbm7rRQy8Y8u/G8GNRsvQEapZRhHRUfnmbSIb/x4VjIiyOUFdBOUrFR6TsqLxvktz1DQgeAAQCNu5V3Kq+iMnvTH+IgAL52Kaqt37cxJdZHMLXQPeBtSnIxjcySb6j0zWQkqzCRPjC3W+mTZH11vlg9Ts+oXC5WD+sXt18UFdL1xuwNbSqFAOofM2Y7P1Kl3spkMpnMF5/v6E86+JH0bgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Exeter","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Georgina","middleName":"L","lastName":"Bennett","suffix":""},{"id":271827561,"identity":"390a8eae-665d-409a-b77b-fe64235b3a99","order_by":1,"name":"Diego Panici","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Panici","suffix":""},{"id":271827562,"identity":"91d5e066-54ae-4adf-9284-9042bd8f6fa0","order_by":2,"name":"Francis Rengers","email":"","orcid":"","institution":"U.S. Geological Survey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Rengers","suffix":""},{"id":271827563,"identity":"35fd0953-c3bc-4b8d-aa37-aeede3093e47","order_by":3,"name":"Jason Kean","email":"","orcid":"","institution":"U.S. Geological Survey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Kean","suffix":""},{"id":271827564,"identity":"33c7e3fe-6599-4c01-8ac4-deb0cd591f00","order_by":4,"name":"Sara Rathburn","email":"","orcid":"","institution":"Colorado State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Rathburn","suffix":""}],"badges":[],"createdAt":"2024-02-07 16:30:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3937459/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3937459/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44304-025-00059-6","type":"published","date":"2025-01-24T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50989239,"identity":"5aa0685c-d5dd-4c59-9ac3-9a8b5835fe5f","added_by":"auto","created_at":"2024-02-12 09:19:47","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1228965,"visible":true,"origin":"","legend":"\u003cp\u003eA – Example of road damage by channel widening in 2013 flood from neighboring South Saint Vrain catchment, image credit Google Earth; B - North Saint Vrain catchment within the Colorado Front Range, Colorado, USA showing September 9 – 15 2013 rainfall totals, landslides triggered by the storm and location of the study reach upstream of Ralph Price Reservoir highlighted in panel C (adapted from Rathburn et al., 2017); C – DEM of difference of study reach in which a sudden increase in channel widening occurred and showing cross sections used to estimate flood peak discharge along transition reach (Figures S6 and S7), refer to Figure S2 for more detail on channel elevation change in this transition reach; L1 and L2 refer to key landslides referred to in the text. Pre- and post-flood channel and the valley floor were mapped manually, as described in the Supplementary Information. Flow in North Saint Vrain Creek is from left to right.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3937459/v1/fe78a01c8c942a9b34f16ef2.jpeg"},{"id":50989238,"identity":"242c0fc6-ccbe-4926-bd16-215355c06324","added_by":"auto","created_at":"2024-02-12 09:19:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99325,"visible":true,"origin":"","legend":"\u003cp\u003eDownstream patterns of cumulative channel erosion and channel widening relative to tributary and landslide sediment inputs and modeled stream power. The confinement ratio is plotted on the secondary y-axis also showing channel widening (m), and a low value of the confinement ratio represents areas that are more confined. Transition reach shown in Figure 1c, Figure 3, and simulated in Figure 4 is highlighted by the gray-shaded area. L1 and L2 refer to key landslides referred to in the text.\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937459/v1/8f10c29912675998c7cd3364.jpg"},{"id":50989240,"identity":"b8eedc58-c939-434c-ba76-acf36205d6ee","added_by":"auto","created_at":"2024-02-12 09:19:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249241,"visible":true,"origin":"","legend":"\u003cp\u003ea – Channel slope along the transition reach shown in Figure 2 showing a decrease in channel slope coinciding with modelled dam formation; b – pre- and post-flood channel profiles and highwater marks collected at 23 cross sections showing wedge of sediment remaining after the flood downstream from L2; c – Q\u003csub\u003epeak\u003c/sub\u003e calculated from high water marks together with lidar (Figures S6 and S7) and Q\u003csub\u003erunoff\u003c/sub\u003e modeled using rainfall data and ratio of these (C). C \u0026gt;1 is indicative of a dam burst event. Dark gray band shows location of modelled dam that formed around the point of entry of L2 (Figure 4). Light gray band shows location of remnants of dam observed in the field (Figure S1).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3937459/v1/ad097d607c47588bfd1b7fee.jpeg"},{"id":50989242,"identity":"32bca6a4-e961-4410-a5fe-305dd301a38b","added_by":"auto","created_at":"2024-02-12 09:19:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1785115,"visible":true,"origin":"","legend":"\u003cp\u003eResults of simulations using r.avaflow: (a) formation of the sediment dam, (b) dam removal with updated topography, (c) the resulting topographic change after both simulations, (d) the results of the simulations with no landslide sediment delivery, highlighting the importance of landslide sediment delivery for simulating observed channel widening.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3937459/v1/0c4715f43a197a4d6990ff94.png"},{"id":74871446,"identity":"18db1944-ae13-4b36-bafc-4673d75cc0ab","added_by":"auto","created_at":"2025-01-27 20:15:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4496667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937459/v1/90f5e8f9-7ee9-4a5c-921a-61d32470b590.pdf"},{"id":50989241,"identity":"c088f6af-3bb4-41d8-aa92-acad0929f3f4","added_by":"auto","created_at":"2024-02-12 09:19:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2573094,"visible":true,"origin":"","legend":"","description":"","filename":"FMBAORevisedSIcleanFKRGB.docx","url":"https://assets-eu.researchsquare.com/files/rs-3937459/v1/acf76a2dd06366d3624fe891.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Landslide-channel feedbacks amplify channel widening during floods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFlood stream power is amplified in mountainous catchments by channel confinement and steep slopes, generating widespread channel erosion and causing substantial challenges for flood risk management in these regions. Approaches to predicting flood channel response include identification of stream power thresholds (Magilligan, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Yochum et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), duration of flow above a critical value (Costa and O Connor, 1995), and downstream gradients in stream power (Gartner et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, other studies have found that hydraulic forces alone are not able to explain geomorphic impacts of floods (e.g., Heritage et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nardi and Rinaldi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Surian et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and highlight the importance of other factors such as human obstructions (Langhammer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); lateral confinement (Thompson and Croke, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sholtes et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ruiz-Villanueva et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); pre-flood channel planform (e.g., Surian et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and channel bed particle packing geometry and stability (e.g., East et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Masteller et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFloods in mountainous catchments often coincide with rainfall-induced landslides on steep valley walls that may deliver substantial volumes of sediment into flooded channels (e.g., Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Abanco et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and downstream receiving waters (Eidmann et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Landslides may interact with and influence channel processes in several ways. First, sediment delivered by landslides may bulk the flow, increasing its bulk density and therefore power, explaining why debris flows and debris floods (Church and Jakob, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) are more erosive than floods (e.g., Brenna et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Landslides may dam channels, partially or completely blocking channel flow for some period, before potentially bursting and causing a powerful and erosive flood downstream (Costa and Schuster, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Korup, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Korup et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ruiz-Villanueva et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Over the long-term, lateral sediment supply from debris flows, landslides, and other sources is known to lead to channel widening due to heightened bed material flux in response to sudden sediment input, or passage of sediment through the channel system as a slug (Miller and Benda, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Cui et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Hoffman and Gabet, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nelson and Dube, 2016; Baynes et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rachelly et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, observations of landslide-channel interactions and their influence on morpho and hydrodynamics are rare, partly due to a lack of evidence left behind in the channel after a flood and the difficulty of observing channel dynamics during a flood. Given that extreme rainfall and associated landslides and flooding are increasing with climate change (Gariano \u0026amp; Guzzetti, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; East et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), such interactions and geomorphic impacts are increasingly important to understand. Here we use high resolution light detection and ranging (lidar) pre- and post-flood digital elevation models (DEMs) together with detailed field analysis and numerical modeling to reconstruct landslide-channel interactions and flood dynamics in a catchment affected by an extreme flood and landsliding.\u003c/p\u003e"},{"header":"Study area and extreme flood and landsliding event","content":"\u003cp\u003eIn September 2013, the Colorado Front Range, USA, experienced a 1000-year rainfall event (Gochis et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Between 9–15 September 2013 the storm dropped up to 500 mm of rain, or 10 times the average September rainfall, in this semi-arid region. The resultant flood, estimated to have a 200-yr recurrence interval (Yochum et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), killed 8 people and caused extensive damage to roads and infrastructure in catchments across the Front Range (e.g., Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The storm also triggered \u0026gt; 1000 shallow landslides and debris flows (Coe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The North Saint Vrain catchment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) received some of the greatest rainfall of any catchment across the range with multiple landslides triggered. The main flood occurred on 11 September once soils were saturated and had an estimated peak discharge at the outlet above Apple Valley Bridge, Lyons, Colorado, of 385 ± 80 m\u003csup\u003e3\u003c/sup\u003e/s (Moody, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The mean annual peak flow is 20 m\u003csup\u003e3\u003c/sup\u003e/s (Wohl et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Rathburn et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) produced a detailed flood sediment budget based on differencing of pre- and post-flood lidar and reservoir coring (pre-flood lidar collected April-October 2011 and post-flood lidar collected October 2013 and July 2014). They found that the flood caused ~ 100 years of erosion and reservoir sedimentation, with half of the erosion occurring through landsliding and with the majority of channel erosion (sediment not bedrock) occurring via channel widening rather than vertical incision (Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This study also indicated a possible role of large wood in avulsions and channel widening associated with the large volume of wood stripped from hillslopes and channel banks. In addition, floodplain disturbance resulting from the 2013 flood coincided with areas of the valley that were less confined (Sutfin and Wohl, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, further questions remain regarding the controls on the variability in channel widening observed along the channel such as the role of landslide-channel coupling and related flood dynamics. The upper North Saint Vrain catchment (upstream from the Ralph Price Reservoir) is a good setting in which to investigate the controls on channel widening as it is one of the few catchments along the Front Range with no human development or obstructions to interrupt hillslope-channel coupling and flood response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHere we investigate the sudden and substantial increase in channel widening and erosion (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) that occurred 7 km downstream from the start of the study channel (due east of Route 7 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The mapping of pre- and post-flood channel widths and valley floor width and calculation of channel widening and erosion is detailed in Supplementary Information. The first 7 km of the study channel reach had little channel erosion (~ 1.4 m\u003csup\u003e3\u003c/sup\u003e/m) and an average widening rate of 5 m or just half of the average preflood channel width (9.94 m), as estimated from the lidar DEM of difference (DoD) (Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The valley floor remained undisturbed at several points (also refer to Sutfin and Wohl, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Downstream from this point, channel erosion (~ 20.7 m\u003csup\u003e3\u003c/sup\u003e/m) was much greater and had an average widening of 28 m (3 times preflood channel width). The flood almost fully occupied the valley floor and so widening was achieved mostly through removal of valley floor sediment, although with some evidence of bedrock erosion on outer bends of the channel (e.g., Figure S5). The most rapid transition occurs at 7 km downstream over a roughly 1.5-km reach, where channel widening reaches 67 m and erosion averages 38 m\u003csup\u003e3\u003c/sup\u003e/m. This transition is not explained by peak flood stream power (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as estimated from radar-based rainfall data (Methods), which increases gradually downstream but does not provide the trigger of the sudden 6 times increase in channel widening. Whilst valley confinement, calculated as the ratio of valley width to pre flood channel width, constrains channel widening beyond 7 km downstream (Figure S3), channel widening upstream from this point is lacking despite relatively unconstrained channels (i.e., confinement ratio \u0026gt; = 3, Sholtes et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) along the entirety of the study channel. However, the sudden increase in channel erosion and widening does occur just downstream from three major lateral sediment inputs from two landslides (L1, L2 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and a tributary, totaling ~ 105,000 m\u003csup\u003e3\u003c/sup\u003e. We used a combination of field data and numerical modeling to investigate the potential mechanisms by which landslide-channel interactions may have resulted in the pattern of channel erosion observed. We tested the hypotheses that the pattern of channel erosion generated by the flood was amplified by (1) bulking of the flow by landslide and tributary sediment; (2) damming of the flow by landslide and tributary sediment, and subsequent failure of this dam resulting in an erosive flood surge similar to that observed in debris flows (Kean et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2 name=\"removable\"\u003eField evidence of landslide-flood interaction\u003c/h2\u003e \u003cp name=\"removable\"\u003eAs an initial test for sediment bulking or surging of the flow that may explain the sudden increase in channel erosion and widening, we calculated a ratio of field-measured to runoff-based peak discharge, C (e.g., Kean et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Flood flows can have a range of C values between 0 and 1 (e.g., Lapides et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to conservation of mass, this ratio cannot exceed 1 in the absence of substantial sediment bulking or surge dynamics and can be used as a check on indirect measurements of flood discharge (e.g., Moody, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A value of C \u0026gt; 1 is diagnostic of bulked and/or surging in the flow that would provide initial support for hypothesis 2 of a landslide dam failure. We visited the reach of increased channel erosion in October 2016 and August 2017 to make indirect measurements of flood peak discharge (Q\u003csub\u003epeak\u003c/sub\u003e) with which to compare our estimated runoff-based discharge (Q\u003csub\u003erunoff\u003c/sub\u003e) (Methods) and calculate the ratio C.\u003c/p\u003e \u003cp name=\"removable\"\u003eWe used a differential Global Positioning System (GPS) to collect pairs of highwater marks at 23 channel cross sections upstream and downstream from the transition reach (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) in 2016 and 2017 with a vertical and horizontal precision of 0.6 m and 0.4 m, respectively. We identified highwater marks based on debris lines on the channel banks and debris trapped in trees. We used the highwater marks within ArcGIS (Esri, Redlands, California) to extract cross sections from lidar data. Where there is deviation in highwater mark elevations between the left and right banks, we propagate this uncertainty into our estimation of peak discharge cross-section area (Figure S6 and S7). Additionally, we calculated cross-section areas using both pre- and post-flood lidar to account for uncertain channel topography during the flood. We calculated peak discharge as\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\" name=\"removable\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${Q}_{peak}=VA$$\u003c/div\u003e\u003c/div\u003e\u003cp name=\"removable\"\u003e\u003c/p\u003e \u003cp name=\"removable\"\u003ewhere \u003cem\u003eA\u003c/em\u003e is cross-section area and \u003cem\u003eV\u003c/em\u003e is velocity. Velocity was calculated using the critical flow method as:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\" name=\"removable\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$V=\\sqrt{gR}$$\u003c/div\u003e\u003c/div\u003e\u003cp name=\"removable\"\u003e\u003c/p\u003e \u003cp name=\"removable\"\u003ewhere \u003cem\u003eR\u003c/em\u003e is hydraulic radius calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R=\\frac{A}{P}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eP\u003c/em\u003e is wetted perimeter and assuming critical flow (i.e., Froude number = 1). The critical flow method has been shown by Moody (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to give values most representative of the average flow conditions during the flood from an ensemble of peak discharge estimation techniques, potentially due to smoothing of the channel bed by sediment infilling during the flood.\u003c/p\u003e \u003cp name=\"removable\"\u003eAlthough Q\u003csub\u003erunoff\u003c/sub\u003e only slightly increases along the transition reach from about 217 to 218 m\u003csup\u003e3\u003c/sup\u003e/s, Q\u003csub\u003epeak\u003c/sub\u003e shows a rapid increase from 72 m\u003csup\u003e3\u003c/sup\u003e/s (± 5 m\u003csup\u003e3\u003c/sup\u003e/s) to 468 m\u003csup\u003e3\u003c/sup\u003e/s (± 190 m\u003csup\u003e3\u003c/sup\u003e/s) over the ~ 300 m downstream from L2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Therefore, the estimated runoff coefficient, C, also increases from 0.33 (± 0.02) to 2.15 (± 0.87), with values \u0026gt; 1 diagnostic of surge type behavior and/or sediment bulking as can occur in debris flows (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) (Kean et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e.\u003c/p\u003e \u003cp name=\"removable\"\u003eDownstream from the L2 landslide entry, a cobble and boulder rich bar is clearly visible in satellite photos (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and was found to have a median grain size of 22 cm (Figure S4). The post-flood channel profile also shows this wedge of sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). We used numerical modelling, detailed below, to test the hypothesis that this bar is what remains of a dam that was formed and then rapidly removed during the flood. The precise timing of landslides that could have caused a valley damming event is uncertain but previous modeling of landslides in the catchment (including L2) indicates landslides occurred shortly after storm rainfall peaked late in the evening on September 11, with landslides on south facing slopes (including L2) occurring late in the evening of September 11 and landslides on north facing slopes occurring in the early morning of September 12 (McGuire et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Witness accounts also record the first landslides happening at this time in neighboring catchments (Coe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ebel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp name=\"removable\"\u003eThe ratio C calculated based on our field observations supports the dam removal hypothesis because the rapid jump in widening at a distinct channel location indicates that a surge of sediment and water similar to a debris flow would be needed to generate erosional widening over a short distance. A dam break could explain the bank erosion by bulking the flow and generating a positive feedback on bank erosion and channel widening. This has been observed for other dam burst events (Costa and Schuster, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), and therefore the dam break hypothesis could cause the observed profound channel widening of up to 67 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2 name=\"removable\"\u003eMulti-phase modeling of landslide-flood interaction\u003c/h2\u003e \u003cp name=\"removable\"\u003eTo further test the hypothesis of a dam formation and burst event, we carried out numerical simulations with the computational model r.avaflow (v. 2.4) (Pudasaini and Mergili, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to simulate the sediment delivery to the valley floor and its interactions with the fluid flow of North Saint Vrain Creek. The computational model r.avaflow is a freely available deterministic, multi-phase model, which is based on the principles of energy and momentum transfer between liquid and solid phases, extending the use of a Voellmy-type resistance model to multiple phases (Pudasaini and Mergili, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mergili et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, r.avaflow has been used to simulate a range of hydro-geomorphic process chains from rock avalanches (Mergili et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), transition of rock avalanches into debris flows (Shugar et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to glacial lake outburst floods triggered by landslides into lakes (Vilca et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For fluvial systems, r.avaflow has been validated for glacial debris flow mobility in mountainous river channels (Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), entrainment of sediment by debris flows in channels (Baggio et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and channel changes from cascading rockslide-channelized debris flow (Mergili et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, to our knowledge it has not before been used to simulate landslide-flood interaction.\u003c/p\u003e \u003cp name=\"removable\"\u003eFor the simulation input, we used the landslides mapped from the DoD for the solid phase and the discharge Q\u003csub\u003erunoff\u003c/sub\u003e for the fluid phase. Because the total simulation time is relatively small (900 seconds in total), the fluid hydrograph has been taken as a constant discharge, corresponding to the peak flow Q\u003csub\u003erunoff\u003c/sub\u003e. Because the landslides were mostly formed by coarse sediment (based on in situ observations after the event), the input sediment phase was considered only as coarse (i.e., friction-dominated for the model computations), whereas no input has been used for the fine phase (i.e., inclusive of viscous effects). Although large wood likely played a role in channel widening in this event (Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), r.avaflow currently does not include this component, but this could be considered in future versions of the model, particularly in the case of hyper-congested flows (Ruiz-Villanueva et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The simulation was performed in two stages due to a limitation of the model in the version used (2.4), where topography is not updated during the model simulation: (1) dam formation is simulated related to triggering of landslides L1 and L2 and delivery of sediment to the channel and topography is updated to include the dam, and (2) dam removal is simulated using updated topography from stage (1). Boundary conditions and flow hydrographs remain the same for both steps. In the model, landslides have been assumed to be released simultaneously. This had minor effects on the simulations because the delivery time to the valley bottom was different for each individual landslide, and all occurred within 30–45 seconds from the start of the simulation. Several sets of simulations have been conducted to explore the parameter space, including mobility and erosive parameters, as well as the hydrographs of the main channel and tributaries (Table S2). Because all parameters are physically based, we used values that are conventionally found in the literature or recommended by the r.avaflow manual (Mergili 2014–2020) depending on the type of event.\u003c/p\u003e \u003cp name=\"removable\"\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the topographic change within the study area at the end of the simulations for both dam formation (first stage) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and dam removal (second stage) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) superimposed on the observed post-event channel extent. Irrespective of the parameters used for the simulations, the model supports the formation of a large sediment dam in the channel downstream from L2, as well as another smaller dam upstream related to L1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Furthermore, when the topography of the area is updated with the deposition (or erosion) heights from the first simulation stage, the second stage model runs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) show that substantial erosion occurs, removing the large dam within the simulation time of 600 seconds. The bulk of the dam is eroded over a period of approximately 300 seconds and leads to the erosion and widening of the channel downstream, due to bulking of the flow by sediment. The final simulated topographic change (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) corresponds well with the observed pattern of erosion from the DoD shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There are some discrepancies between the simulation and observations. For example, the simulated dam occurs closer to the toe of the landslide L2 than the remnants of the dam we observed in the field, which occur ~ 100 m downstream (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, these are to be expected considering challenges in replicating spatially explicit two- and three-dimensional landscape evolution in fluvial morphodynamic models (e.g., Lauer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp name=\"removable\"\u003eTo further test the hypothesis that the topographic change of the channel was caused by the formation and then removal of the sediment dam, we performed an additional simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) without landslide sediment input, to evaluate whether the flood flow alone and the sediment it mobilized from upstream to downstream (i.e., not including landslide sediment input) could have caused the channel change. In this scenario, only limited erosion (i.e., \u0026lt; 0.10 m) was observed in the main channel downstream from L2 and the extent was substantially smaller than the post-event observations. This is in contrast with the simulations inclusive of landslide input (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), in which the post-event channel extents were much more consistent with the observed changes. We therefore conclude that a landslide-dam break and rapid delivery of sediment best explains the observed geomorphic channel change during the flood. All the numerical simulations involving landslides qualitatively agree on the formation and removal of a dam, whereby the downstream channel widening resulted from bulking of the flow by sediment related to a removal of the debris dam.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2 name=\"removable\"\u003eLandslide-flood interaction amplified flood channel widening\u003c/h2\u003e \u003cp name=\"removable\"\u003ePrevious studies have documented cycles of hillslope-channel coupling over multi-event timescales with hillslopes delivering sediment to the channel during a rainstorm, for example, and subsequent floods clearing this from the channel system (e.g., Harvey et al., 2001; Berger et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bennett et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Other studies have documented the occurrence of landslide-dams and the erosive power of dam burst floods, whether landslide induced or otherwise (Costa and Schuster, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, Jarrett and Costa, 1986). Here, we have documented with a combination of detailed field data analysis and numerical modeling how landslide-delivered sediment may interact with the main channel during an individual flood through formation and failure of a dam and bulking of the flow by sediment. In agreement with prior studies, we observed that landslides delivering sediment into a fluvial channel led to channel widening (Nelson and Dube, 2016; Cui et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Hoffman and Gabet, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Miller and Benda, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Baynes et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rachelly et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp name=\"removable\"\u003eHere we outline that transition with the following stages. In Stage 1, landslides triggered by heavy rainfall deliver sediment to the flood flow and temporarily dam the channel. In Stage 2, the flood starts to gradually remove the dam, bulking the flow with sediment and causing it to erode channel banks downstream. In Stage 3, the flow continues to be loaded with sediment from the dam and from bank erosion causing further erosion of outer banks, although remnants of the dam remain. Although it is likely that large wood played a role in the pattern of channel widening (Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as observed in other catchments, modeling indicates that sediment delivered by landslides to the flood flow is crucial for explaining the substantial channel widening observed in this event (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,d). We have focused here on the damming and erosion related to the larger landslide L2, although we note that farther upstream landslide L1 also forms a small dam at its toe in the model and probably explains the channel widening of the opposite bank of the river at that location (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp name=\"removable\"\u003eWe have focused on landslide sediment delivery directly into the study channel but sediment input from tributaries may result in similar effects. For example, at ~ 13 km downstream a tributary delivered ~ 5 x 10\u003csup\u003e4\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e of sediment (predominantly from landslides along the tributary) potentially damming and/or bulking the flow and resulting in the peak in channel widening just downstream. We suspect that similar landslide-channel interactions may be responsible for higher than expected peak discharge relative to rainfall runoff in other Colorado Front Range catchments flooded in 2013 (Moody, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, such landslide-channel interactions may be relevant for understanding and predicting channel widening and erosion during floods in other mountainous regions of the world. Given that extreme rainfall and associated landslides and flooding are expected to increase in a warmer climate (East et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that extreme rainfall can be more important in generating landslides than earthquakes, even in places that are tectonically active (Jones et al., 2022; LaHusen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Odin et al., 2019), such landslide-flood interactions will be increasingly important to account for.\u003c/p\u003e \u003c/div\u003e"},{"header":"Field evidence of landslide-flood interaction","content":"\u003cp\u003eAs an initial test for sediment bulking or surging of the flow that may explain the sudden increase in channel erosion and widening, we calculated a ratio of field-measured to runoff-based peak discharge, C (e.g., Kean et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Flood flows can have a range of C values between 0 and 1 (e.g., Lapides et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to conservation of mass, this ratio cannot exceed 1 in the absence of substantial sediment bulking or surge dynamics and can be used as a check on indirect measurements of flood discharge (e.g., Moody, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A value of C \u0026gt; 1 is diagnostic of bulked and/or surging in the flow that would provide initial support for hypothesis 2 of a landslide dam failure. We visited the reach of increased channel erosion in October 2016 and August 2017 to make indirect measurements of flood peak discharge (Q\u003csub\u003epeak\u003c/sub\u003e) with which to compare our estimated runoff-based discharge (Q\u003csub\u003erunoff\u003c/sub\u003e) (Methods) and calculate the ratio C.\u003c/p\u003e\u003cp\u003eWe used a differential Global Positioning System (GPS) to collect pairs of highwater marks at 23 channel cross sections upstream and downstream from the transition reach (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) in 2016 and 2017 with a vertical and horizontal precision of 0.6 m and 0.4 m, respectively. We identified highwater marks based on debris lines on the channel banks and debris trapped in trees. We used the highwater marks within ArcGIS (Esri, Redlands, California) to extract cross sections from lidar data. Where there is deviation in highwater mark elevations between the left and right banks, we propagate this uncertainty into our estimation of peak discharge cross-section area (Figure S6 and S7). Additionally, we calculated cross-section areas using both pre- and post-flood lidar to account for uncertain channel topography during the flood. We calculated peak discharge as\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${Q}_{peak}=VA$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eA\u003c/em\u003e is cross-section area and \u003cem\u003eV\u003c/em\u003e is velocity. Velocity was calculated using the critical flow method as:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$V=\\sqrt{gR}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eR\u003c/em\u003e is hydraulic radius calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R=\\frac{A}{P}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eP\u003c/em\u003e is wetted perimeter and assuming critical flow (i.e., Froude number = 1). The critical flow method has been shown by Moody (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to give values most representative of the average flow conditions during the flood from an ensemble of peak discharge estimation techniques, potentially due to smoothing of the channel bed by sediment infilling during the flood.\u003c/p\u003e\u003cp\u003eAlthough Q\u003csub\u003erunoff\u003c/sub\u003e only slightly increases along the transition reach from about 217 to 218 m\u003csup\u003e3\u003c/sup\u003e/s, Q\u003csub\u003epeak\u003c/sub\u003e shows a rapid increase from 72 m\u003csup\u003e3\u003c/sup\u003e/s (± 5 m\u003csup\u003e3\u003c/sup\u003e/s) to 468 m\u003csup\u003e3\u003c/sup\u003e/s (± 190 m\u003csup\u003e3\u003c/sup\u003e/s) over the ~ 300 m downstream from L2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Therefore, the estimated runoff coefficient, C, also increases from 0.33 (± 0.02) to 2.15 (± 0.87), with values \u0026gt; 1 diagnostic of surge type behavior and/or sediment bulking as can occur in debris flows (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) (Kean et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eDownstream from the L2 landslide entry, a cobble and boulder rich bar is clearly visible in satellite photos (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and was found to have a median grain size of 22 cm (Figure S4). The post-flood channel profile also shows this wedge of sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). We used numerical modelling, detailed below, to test the hypothesis that this bar is what remains of a dam that was formed and then rapidly removed during the flood. The precise timing of landslides that could have caused a valley damming event is uncertain but previous modeling of landslides in the catchment (including L2) indicates landslides occurred shortly after storm rainfall peaked late in the evening on September 11, with landslides on south facing slopes (including L2) occurring late in the evening of September 11 and landslides on north facing slopes occurring in the early morning of September 12 (McGuire et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Witness accounts also record the first landslides happening at this time in neighboring catchments (Coe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ebel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe ratio C calculated based on our field observations supports the dam removal hypothesis because the rapid jump in widening at a distinct channel location indicates that a surge of sediment and water similar to a debris flow would be needed to generate erosional widening over a short distance. A dam break could explain the bank erosion by bulking the flow and generating a positive feedback on bank erosion and channel widening. This has been observed for other dam burst events (Costa and Schuster, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), and therefore the dam break hypothesis could cause the observed profound channel widening of up to 67 m.\u003c/p\u003e"},{"header":"Multi-phase modeling of landslide-flood interaction","content":"\u003cp\u003eTo further test the hypothesis of a dam formation and burst event, we carried out numerical simulations with the computational model r.avaflow (v. 2.4) (Pudasaini and Mergili, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to simulate the sediment delivery to the valley floor and its interactions with the fluid flow of North Saint Vrain Creek. The computational model r.avaflow is a freely available deterministic, multi-phase model, which is based on the principles of energy and momentum transfer between liquid and solid phases, extending the use of a Voellmy-type resistance model to multiple phases (Pudasaini and Mergili, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mergili et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, r.avaflow has been used to simulate a range of hydro-geomorphic process chains from rock avalanches (Mergili et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), transition of rock avalanches into debris flows (Shugar et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to glacial lake outburst floods triggered by landslides into lakes (Vilca et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For fluvial systems, r.avaflow has been validated for glacial debris flow mobility in mountainous river channels (Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), entrainment of sediment by debris flows in channels (Baggio et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and channel changes from cascading rockslide-channelized debris flow (Mergili et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, to our knowledge it has not before been used to simulate landslide-flood interaction.\u003c/p\u003e\u003cp\u003eFor the simulation input, we used the landslides mapped from the DoD for the solid phase and the discharge Q\u003csub\u003erunoff\u003c/sub\u003e for the fluid phase. Because the total simulation time is relatively small (900 seconds in total), the fluid hydrograph has been taken as a constant discharge, corresponding to the peak flow Q\u003csub\u003erunoff\u003c/sub\u003e. Because the landslides were mostly formed by coarse sediment (based on in situ observations after the event), the input sediment phase was considered only as coarse (i.e., friction-dominated for the model computations), whereas no input has been used for the fine phase (i.e., inclusive of viscous effects). Although large wood likely played a role in channel widening in this event (Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), r.avaflow currently does not include this component, but this could be considered in future versions of the model, particularly in the case of hyper-congested flows (Ruiz-Villanueva et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The simulation was performed in two stages due to a limitation of the model in the version used (2.4), where topography is not updated during the model simulation: (1) dam formation is simulated related to triggering of landslides L1 and L2 and delivery of sediment to the channel and topography is updated to include the dam, and (2) dam removal is simulated using updated topography from stage (1). Boundary conditions and flow hydrographs remain the same for both steps. In the model, landslides have been assumed to be released simultaneously. This had minor effects on the simulations because the delivery time to the valley bottom was different for each individual landslide, and all occurred within 30–45 seconds from the start of the simulation. Several sets of simulations have been conducted to explore the parameter space, including mobility and erosive parameters, as well as the hydrographs of the main channel and tributaries (Table S2). Because all parameters are physically based, we used values that are conventionally found in the literature or recommended by the r.avaflow manual (Mergili 2014–2020) depending on the type of event.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the topographic change within the study area at the end of the simulations for both dam formation (first stage) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and dam removal (second stage) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) superimposed on the observed post-event channel extent. Irrespective of the parameters used for the simulations, the model supports the formation of a large sediment dam in the channel downstream from L2, as well as another smaller dam upstream related to L1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Furthermore, when the topography of the area is updated with the deposition (or erosion) heights from the first simulation stage, the second stage model runs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) show that substantial erosion occurs, removing the large dam within the simulation time of 600 seconds. The bulk of the dam is eroded over a period of approximately 300 seconds and leads to the erosion and widening of the channel downstream, due to bulking of the flow by sediment. The final simulated topographic change (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) corresponds well with the observed pattern of erosion from the DoD shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There are some discrepancies between the simulation and observations. For example, the simulated dam occurs closer to the toe of the landslide L2 than the remnants of the dam we observed in the field, which occur ~ 100 m downstream (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, these are to be expected considering challenges in replicating spatially explicit two- and three-dimensional landscape evolution in fluvial morphodynamic models (e.g., Lauer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTo further test the hypothesis that the topographic change of the channel was caused by the formation and then removal of the sediment dam, we performed an additional simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) without landslide sediment input, to evaluate whether the flood flow alone and the sediment it mobilized from upstream to downstream (i.e., not including landslide sediment input) could have caused the channel change. In this scenario, only limited erosion (i.e., \u0026lt; 0.10 m) was observed in the main channel downstream from L2 and the extent was substantially smaller than the post-event observations. This is in contrast with the simulations inclusive of landslide input (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), in which the post-event channel extents were much more consistent with the observed changes. We therefore conclude that a landslide-dam break and rapid delivery of sediment best explains the observed geomorphic channel change during the flood. All the numerical simulations involving landslides qualitatively agree on the formation and removal of a dam, whereby the downstream channel widening resulted from bulking of the flow by sediment related to a removal of the debris dam.\u003c/p\u003e"},{"header":"Landslide-flood interaction amplified flood channel widening","content":"\u003cp\u003ePrevious studies have documented cycles of hillslope-channel coupling over multi-event timescales with hillslopes delivering sediment to the channel during a rainstorm, for example, and subsequent floods clearing this from the channel system (e.g., Harvey et al., 2001; Berger et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bennett et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Other studies have documented the occurrence of landslide-dams and the erosive power of dam burst floods, whether landslide induced or otherwise (Costa and Schuster, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, Jarrett and Costa, 1986). Here, we have documented with a combination of detailed field data analysis and numerical modeling how landslide-delivered sediment may interact with the main channel during an individual flood through formation and failure of a dam and bulking of the flow by sediment. In agreement with prior studies, we observed that landslides delivering sediment into a fluvial channel led to channel widening (Nelson and Dube, 2016; Cui et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Hoffman and Gabet, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Miller and Benda, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Baynes et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rachelly et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHere we outline that transition with the following stages. In Stage 1, landslides triggered by heavy rainfall deliver sediment to the flood flow and temporarily dam the channel. In Stage 2, the flood starts to gradually remove the dam, bulking the flow with sediment and causing it to erode channel banks downstream. In Stage 3, the flow continues to be loaded with sediment from the dam and from bank erosion causing further erosion of outer banks, although remnants of the dam remain. Although it is likely that large wood played a role in the pattern of channel widening (Rathburn et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as observed in other catchments, modeling indicates that sediment delivered by landslides to the flood flow is crucial for explaining the substantial channel widening observed in this event (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,d). We have focused here on the damming and erosion related to the larger landslide L2, although we note that farther upstream landslide L1 also forms a small dam at its toe in the model and probably explains the channel widening of the opposite bank of the river at that location (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eWe have focused on landslide sediment delivery directly into the study channel but sediment input from tributaries may result in similar effects. For example, at ~ 13 km downstream a tributary delivered ~ 5 x 10\u003csup\u003e4\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e of sediment (predominantly from landslides along the tributary) potentially damming and/or bulking the flow and resulting in the peak in channel widening just downstream. We suspect that similar landslide-channel interactions may be responsible for higher than expected peak discharge relative to rainfall runoff in other Colorado Front Range catchments flooded in 2013 (Moody, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, such landslide-channel interactions may be relevant for understanding and predicting channel widening and erosion during floods in other mountainous regions of the world. Given that extreme rainfall and associated landslides and flooding are expected to increase in a warmer climate (East et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that extreme rainfall can be more important in generating landslides than earthquakes, even in places that are tectonically active (Jones et al., 2022; LaHusen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Odin et al., 2019), such landslide-flood interactions will be increasingly important to account for.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.B. conceived the study with input from S.R., J.K. and F.R. G.B., J.K., and F.R., conducted fieldwork. G.B. performed analysis of lidar and field data with input from J.K. and F.R. D.P. conducted r.avaflow modeling. G.B. wrote the manuscript with contributions from D.P., F.R., J.K., and S.R. G.B. prepared figures 1 - 3 and D.P. prepared figure 4. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eAny use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbanco, C., Bennett, G. L., Matthews, A. J., Matera, M. A., \u0026amp; Tan, F. J. The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines. Nat. Hazards Earth Syst. 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Geomorphology. 292 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geomorph.2017.03.004\u003c/span\u003e\u003cspan address=\"10.1016/j.geomorph.2017.03.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3937459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3937459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChannel widening is a major hazard during floods, particularly in confined mountainous catchments where roads and buildings compete for space with river channels. Flood-induced channel widening is also an important process in eroding and shaping the landscape. However, channel widening during floods is not well understood and not always explained by hydraulic variables alone, with implications for flood risk management. Floods in mountainous regions often coincide with landslides triggered by heavy rainfall on steep valley sides. Whilst the long-term impact of increased sediment supply on channel widening is well established, landslide-channel interactions at the event timescale are not well known or documented. Here we demonstrate with an example from the Great Colorado Flood in 2013, a 1000-yr precipitation event that induced a 200-yr flood, and 100-yrs of erosion, how landslide-channel feedbacks can substantially amplify channel widening and flood risk. We use a combination of field analysis and multiphase flow modeling to document landslide-channel interaction during the flood event in which sediment delivered by landslides temporarily dammed the channel before failing and bulking the flow with sediment resulting in large channel widening. We propose that such landslide-flood interactions will become increasingly important to account for in flood hazard assessment as flooding and landsliding increase with extreme rainfall under climate change.\u003c/p\u003e","manuscriptTitle":"Landslide-channel feedbacks amplify channel widening during floods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 09:19:43","doi":"10.21203/rs.3.rs-3937459/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-08T17:25:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-08T17:21:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-08T12:01:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Natural Hazards","date":"2024-02-07T16:20:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96195dcb-f56e-4394-8110-d2167f1b7a2e","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28664358,"name":"Earth and environmental sciences/Natural hazards"},{"id":28664359,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-01-27T20:15:42+00:00","versionOfRecord":{"articleIdentity":"rs-3937459","link":"https://doi.org/10.1038/s44304-025-00059-6","journal":{"identity":"npj-natural-hazards","isVorOnly":false,"title":"npj Natural Hazards"},"publishedOn":"2025-01-24 00:00:00","publishedOnDateReadable":"January 24th, 2025"},"versionCreatedAt":"2024-02-12 09:19:43","video":"","vorDoi":"10.1038/s44304-025-00059-6","vorDoiUrl":"https://doi.org/10.1038/s44304-025-00059-6","workflowStages":[]},"version":"v1","identity":"rs-3937459","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3937459","identity":"rs-3937459","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}