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ERROR: type should be string, got "https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Abstract. exceptionally high rates of drainage reorganization driven by horizontal shortening and propagating uplift. Here we determine \n10 \nthe prevalence, intensity, and spatial patterns of ongoing drainage reorganization in the Hengduan Mountains and evaluate the \nrelative time scales of this transience by comparing drainage divide asymmetry for four geomorphic metrics that operate at \ndifferent spatial and temporal scales. Specifically, we calculate the migration direction and the divide asymmetry index (DAI) \ndrainage divide asymmetry in catchment-restricted topographic relief (CRR), hillslope gradient (HSG), normalized channel exceptionally high rates of drainage reorganization driven by horizontal shortening and propagating uplift. Here we determine \n10 \nthe prevalence, intensity, and spatial patterns of ongoing drainage reorganization in the Hengduan Mountains and evaluate the \nrelative time scales of this transience by comparing drainage divide asymmetry for four geomorphic metrics that operate at \ndifferent spatial and temporal scales. Specifically, we calculate the migration direction and the divide asymmetry index (DAI) \ndrainage divide asymmetry in catchment-restricted topographic relief (CRR), hillslope gradient (HSG), normalized channel \nsteepness (ksn), and normalized channel distance (χ). ksn and χ are both precipitation-corrected to account for the strong \n15 \nprecipitation gradient across the region. The different spatial scales of these geomorphic metrics allow us to establish the \nrelative timescales of observed landscape transience in the Hengduan Mountains, where local scale metrics measure short-\nterm change and integral quantities measure long-term disequilibrium. We find a high incidence of strongly asymmetric divides \nin all metrics across the Hengduan Mountain region. Although the magnitude of asymmetry varies significantly between \nmetrics, possibly due to a combination of metric-specific thresholds and varying proxy relationships with erosion rate, a \n20 \nmajority of divides agree on divide migration direction. Agreement in divide migration direction indicates active landscape \nresponse when asymmetry is high and a state of quasi-equilibrium when asymmetry is low. Disagreements between the integral \nquantity, χ, and the other geomorphic metrics can be explained by different timescales of the underlying geomorphic processes, \nwith χ reflecting a long-term response and CRR, HSG, and ksn capturing short-term perturbations to catchment structure. These \nperturbations include various transient mechanisms, such as differential tectonic uplift or erodibility, glacial alteration, and \n25 \nriver captures. Our work confirms the high incidence of drainage reorganization across the Hengduan Mountains and highlights g\ny\ny\np g p\np g\nsteepness (ksn), and normalized channel distance (χ). Abstract. ksn and χ are both precipitation-corrected to account for the strong \n15 \nprecipitation gradient across the region. The different spatial scales of these geomorphic metrics allow us to establish the \nrelative timescales of observed landscape transience in the Hengduan Mountains, where local scale metrics measure short-\nterm change and integral quantities measure long-term disequilibrium. We find a high incidence of strongly asymmetric divides \nin all metrics across the Hengduan Mountain region. Although the magnitude of asymmetry varies significantly between steepness (ksn), and normalized channel distance (χ). ksn and χ are both precipitation-corrected to account for the strong \n15 \nprecipitation gradient across the region. The different spatial scales of these geomorphic metrics allow us to establish the \nrelative timescales of observed landscape transience in the Hengduan Mountains, where local scale metrics measure short-\nterm change and integral quantities measure long-term disequilibrium. We find a high incidence of strongly asymmetric divides \nin all metrics across the Hengduan Mountain region. Although the magnitude of asymmetry varies significantly between metrics, possibly due to a combination of metric-specific thresholds and varying proxy relationships with erosion rate, a \n20 \nmajority of divides agree on divide migration direction. Agreement in divide migration direction indicates active landscape \nresponse when asymmetry is high and a state of quasi-equilibrium when asymmetry is low. Disagreements between the integral \nquantity, χ, and the other geomorphic metrics can be explained by different timescales of the underlying geomorphic processes, \nwith χ reflecting a long-term response and CRR, HSG, and ksn capturing short-term perturbations to catchment structure. These perturbations include various transient mechanisms, such as differential tectonic uplift or erodibility, glacial alteration, and \n25 \nriver captures. Our work confirms the high incidence of drainage reorganization across the Hengduan Mountains and highlights \nboth transient and stable areas in the landscape with high resolution. We also offer valuable insights on the application of \ngeomorphic metrics that can be generalized and applied to the study of landscape transience and drainage divide asymmetry \nin other settings. perturbations include various transient mechanisms, such as differential tectonic uplift or erodibility, glacial alteration, and \n25 \nriver captures. Our work confirms the high incidence of drainage reorganization across the Hengduan Mountains and highlights \nboth transient and stable areas in the landscape with high resolution. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Abstract. We also offer valuable insights on the application of \ngeomorphic metrics that can be generalized and applied to the study of landscape transience and drainage divide asymmetry \nin other settings. 1 1 1 Introduction \n30 The Earth's landscapes are constantly evolving as a result of the complex interplay between surface processes and \nexternal forcing, such as climate and tectonics. Landscape change occurs through both geometric transience, which we use to \nrefer to planform changes in the drainage network, and topographic transience, which refers to changes in relief or elevation. Planform drainage network geometric (henceforth “geometric”) and topographic transience are closely linked, as changes in \ndrainage patterns can have a significant impact on the relief and elevation of the landscape and, reciprocally, changes in \n35 \ntopography can trigger changes to the drainage network. 35 Figure 1: (a) Elevation map of HDM region showing major faults in black. Rivers are shown in blue and labelled in black. White \nlabels denote key areas mentioned in text. (b) Topographic map of Central Asia; white inset box shows extent of a & c. (c) Map of \nmean annual precipitation in the HDM (Karger et al., 2017, 2018). Rivers are shown in black. Figure 1: (a) Elevation map of HDM region showing major faults in black. Rivers are shown in blue and labelled in black. White \nlabels denote key areas mentioned in text. (b) Topographic map of Central Asia; white inset box shows extent of a & c. (c) Map of \nmean annual precipitation in the HDM (Karger et al., 2017, 2018). Rivers are shown in black. 40 A prime example of this connection between geometric network change and topography is observed in the high-\nelevation, low-relief areas scattered throughout the Hengduan Mountains (HDM), Southeast Tibet. Many of these low-relief \nfeatures are interpreted to result from river capture, where drainage area loss inhibits the ability of catchment erosion to keep \npace with background uplift (Yang et al., 2015; Yuan et al., 2021). This hypothesis is supported by several major river captures A prime example of this connection between geometric network change and topography is observed in the high-\nelevation, low-relief areas scattered throughout the Hengduan Mountains (HDM), Southeast Tibet. Many of these low-relief \nfeatures are interpreted to result from river capture, where drainage area loss inhibits the ability of catchment erosion to keep \npace with background uplift (Yang et al., 2015; Yuan et al., 2021). This hypothesis is supported by several major river captures \nin the HDM which indicate significant, ongoing drainage reorganization in the region (Clark et al., 2004; Zheng et al., 2021). 1 Introduction \n30 45 in the HDM which indicate significant, ongoing drainage reorganization in the region (Clark et al., 2004; Zheng et al., 2021). 45 \nWhile few of these captures have been decisively dated, several have been confirmed or estimated to have occurred in the last 2 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 2-4 Ma (Kong et al., 2012; Liu et al., 2020; Sun et al., 2020; Yang et al., 2020). Alternative explanations for these low-relief \nfeatures include a delayed incisional response in small tributaries to propagating tectonic uplift from the ongoing India-Eurasia \ncollision (Clark et al., 2006) and/or glacial planation (Zhang et al., 2016). Despite its critical role in shaping the landscape, the \nprevalence, intensity, and spatial distribution of geometric transience has not been systematically measured across the HDM \n50 \non a large scale. 50 The geometry of a drainage network moves towards a quasi-topographic equilibrium as the system adjusts to spatial \nasymmetries in erosion and uplift (Beeson et al., 2017; Adams et al., 2020). This happens through drainage divide migration, \nwhich can occur by both continuous shift in the position of a divide and discrete river capture events. As the direct measurement \nof catchment erosion rates is difficult, previous studies rely primarily on across-divide differences in geomorphic steepness \n55 \nmetrics as a proxy for differential erosion to infer active drainage exchange between catchments. Common metrics include \nmean hillslope gradient, mean local relief, stream channel steepness, channel head elevation, and hillslope curvature measured \nnear the divide (Hurst et al., 2013; Whipple et al., 2017; Scherler and Schwanghart, 2020). of catchment erosion rates is difficult, previous studies rely primarily on across-divide differences in geomorphic steepness \n55 \nmetrics as a proxy for differential erosion to infer active drainage exchange between catchments. Common metrics include \nmean hillslope gradient, mean local relief, stream channel steepness, channel head elevation, and hillslope curvature measured \nnear the divide (Hurst et al., 2013; Whipple et al., 2017; Scherler and Schwanghart, 2020). I\nddi i\nh\nl\nl\nl\ni\nf\nd\ni bl\nf h\nl\ndi\n(P\nd R\nd of catchment erosion rates is difficult, previous studies rely primarily on across-divide differences in geomorphic steepness \n55 \nmetrics as a proxy for differential erosion to infer active drainage exchange between catchments. Common metrics include \nmean hillslope gradient, mean local relief, stream channel steepness, channel head elevation, and hillslope curvature measured \nnear the divide (Hurst et al., 2013; Whipple et al., 2017; Scherler and Schwanghart, 2020). In addition to these local-scale metrics χ a transformed variable of the along-stream distance (Perron and Royden In addition to these local scale metrics, χ, a transformed variable of the along stream distance (Perron and Royden, \n2013), has been widely applied to assess the general geometric stability of the drainage network pattern with the assumption \n60 \nthat planform patterns in χ should be reflected in the distribution of divide elevation and symmetry (Willett et al., 2014; Beeson \net al., 2017). However, the direct interpretation of χ as a proxy for steady-state divide elevation requires that rock uplift and \nerodibility have the same mean values, integrated along the channel (Perron and Royden, 2013; Willett et al., 2014), which is \nunlikely to hold over the scale of entire mountain ranges at any given time. While χ is easily adjusted to account for variable 2013), has been widely applied to assess the general geometric stability of the drainage network pattern with the assumption \n60 \nthat planform patterns in χ should be reflected in the distribution of divide elevation and symmetry (Willett et al., 2014; Beeson \net al., 2017). However, the direct interpretation of χ as a proxy for steady-state divide elevation requires that rock uplift and \nerodibility have the same mean values, integrated along the channel (Perron and Royden, 2013; Willett et al., 2014), which is \nunlikely to hold over the scale of entire mountain ranges at any given time. While χ is easily adjusted to account for variable 2013), has been widely applied to assess the general geometric stability of the drainage network pattern with the assumption \n60 \nthat planform patterns in χ should be reflected in the distribution of divide elevation and symmetry (Willett et al., 2014; Beeson \net al., 2017). However, the direct interpretation of χ as a proxy for steady-state divide elevation requires that rock uplift and \nerodibility have the same mean values, integrated along the channel (Perron and Royden, 2013; Willett et al., 2014), which is \nunlikely to hold over the scale of entire mountain ranges at any given time. While χ is easily adjusted to account for variable erodibility due to spatial patterns in precipitation (Yang et al., 2015), other effects, such as lithology and local variations in \n65 \nuplift, remain challenging to incorporate (Wu et al., 2022; Zhou et al., 2022; Dal Pai et al., 2023). 80 The particular case of spatial disagreement between χ and local-scale metrics is investigated in more depth to \ndetermine if disagreement is due to violation of the assumption of constant mean uplift and erodibility or reflects cases where \nlong-term transience has not propagated through the catchment to reach the divide. 90 90 2 Background The HDM are a series of northwest-southeast oriented mountain ranges extending from the south-eastern margin of the Tibetan \nPlateau (Fig. 1a,b). The region encompasses ~600,000 km2 and has highly variable relief, with elevations ranging from less \nthan 300 m to over 5,000 m. The highest peak, Gongga Shan, reaches 7,470 m and lies within the Daxue Shan range on the \nHDM’s eastern flank. 95 HDM’s eastern flank. 5 In addition, elevation \ntransients in the stream network, e.g., from a change in uplift rate, require time to propagate upstream to water divides. As χ \nrepresents the equilibrium elevation and not the instantaneous elevation, it will not accurately reflect true channel head \nelevation or steepness during elevation transients. Metrics which reflect local erosion and uplift dynamics are thus more reliable erodibility due to spatial patterns in precipitation (Yang et al., 2015), other effects, such as lithology and local variations in \n65 \nuplift, remain challenging to incorporate (Wu et al., 2022; Zhou et al., 2022; Dal Pai et al., 2023). In addition, elevation \ntransients in the stream network, e.g., from a change in uplift rate, require time to propagate upstream to water divides. As χ \nrepresents the equilibrium elevation and not the instantaneous elevation, it will not accurately reflect true channel head \nelevation or steepness during elevation transients. Metrics which reflect local erosion and uplift dynamics are thus more reliable predictors of instantaneous motion of specific drainage divides at a specific time (Whipple et al., 2017; Dal Pai et al., 2023). 70 \nHowever, χ may still indicate future divide instabilities within a catchment, even in landscapes with variable erodibility and \nuplift (Forte and Whipple, 2018), as eventually uplift rates and erodibility are likely to equilibrate and transients will propagate \nthrough the network. predictors of instantaneous motion of specific drainage divides at a specific time (Whipple et al., 2017; Dal Pai et al., 2023). 70 \nHowever, χ may still indicate future divide instabilities within a catchment, even in landscapes with variable erodibility and \nuplift (Forte and Whipple, 2018), as eventually uplift rates and erodibility are likely to equilibrate and transients will propagate \nthrough the network. Local-scale metrics are also subject to variations in local physical properties and transients and regularly exhibit large \nvariability along drainage divides, as well as internal contradictions between metrics (Dal Pai et al., 2023). To mitigate this, \n75 \nstudies often combine multiple metrics and/or take the mean value of all catchments along each side of a main drainage divide \n(Forte and Whipple, 2018). However, the underlying mechanisms responsible for variability within or between metrics that \nmeasure local or regionally integrated quantities are likely to be different and resolution of this variability requires a systematic \nevaluation in transient landscapes. 3 3 2.1 Drainage Network The HDM are dissected by several major rivers, most notably the Yangtze (Jinsha), Mekong (Lancang), and Salween (Nu), \nwhose headwaters originate on the Tibetan Plateau (Fig. 1a). These three rivers form steep gorges which run parallel to each \nother for over 1,000 km (Fig. 1a, “Three Rivers”), before carving divergent paths across the landscape and eventually draining The HDM are dissected by several major rivers, most notably the Yangtze (Jinsha), Mekong (Lancang), and Salween (Nu), \nwhose headwaters originate on the Tibetan Plateau (Fig. 1a). These three rivers form steep gorges which run parallel to each \nother for over 1,000 km (Fig. 1a, “Three Rivers”), before carving divergent paths across the landscape and eventually draining \nto the East China, South China, and Andaman seas, respectively. River capture has significantly altered the drainage network \n100 \nin the HDM (Clark et al., 2004), however the timing and drivers of landscape transience are still debated, with hypotheses \nincluding the onset of regional surface uplift and strike-slip faulting in the Eocene (Clark et al., 2004; Gourbet et al., 2017), an \nintensified monsoon in the Miocene (Zheng et al., 2021), volcanic activity (Gourbet et al., 2017), and/or an increase in localized \nuplift rate in the Plio-Pleistocene (Yang et al., 2020). Documented major captures include the rerouting of the upper Yangtze \nfrom the paleo-Red River sometime between the Eocene and Pleistocene (Kong et al., 2012; Sun et al., 2020; Liu et al., 2020; \n105 other for over 1,000 km (Fig. 1a, Three Rivers ), before carving divergent paths across the landscape and eventually draining \nto the East China, South China, and Andaman seas, respectively. River capture has significantly altered the drainage network \n100 \nin the HDM (Clark et al., 2004), however the timing and drivers of landscape transience are still debated, with hypotheses \nincluding the onset of regional surface uplift and strike-slip faulting in the Eocene (Clark et al., 2004; Gourbet et al., 2017), an \nintensified monsoon in the Miocene (Zheng et al., 2021), volcanic activity (Gourbet et al., 2017), and/or an increase in localized \nuplift rate in the Plio-Pleistocene (Yang et al., 2020). Documented major captures include the rerouting of the upper Yangtze 100 to the East China, South China, and Andaman seas, respectively. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. This study aims to determine the prevalence, intensity, and spatial patterns of ongoing geometric transience in the \n80 \nHDM by evaluating drainage divide asymmetry using four geomorphic metrics that operate at different spatial and, \nconsequently, temporal scales. Our approach enables us to differentiate between transient and relatively stable sections of the \nHDM drainage network and deepen our understanding of the mechanisms, drivers, and relative time scales of landscape \nevolution in this dynamic region. We also take the opportunity to compare the internal self-consistency of geomorphic metrics \nin predictions of drainage divide migration across the tectonically and climatically diverse HDM (Fig. 1). We examine the \n85 \ndegree of agreement between metrics on the direction of divide migration and the impact of asymmetry magnitude on that \nagreement. The particular case of spatial disagreement between χ and local-scale metrics is investigated in more depth to \ndetermine if disagreement is due to violation of the assumption of constant mean uplift and erodibility or reflects cases where \nlong-term transience has not propagated through the catchment to reach the divide. 90 This study aims to determine the prevalence, intensity, and spatial patterns of ongoing geometric transience in the \n80 \nHDM by evaluating drainage divide asymmetry using four geomorphic metrics that operate at different spatial and, \nconsequently, temporal scales. Our approach enables us to differentiate between transient and relatively stable sections of the \nHDM drainage network and deepen our understanding of the mechanisms, drivers, and relative time scales of landscape \nevolution in this dynamic region. We also take the opportunity to compare the internal self-consistency of geomorphic metrics \nin predictions of drainage divide migration across the tectonically and climatically diverse HDM (Fig. 1). We examine the \n85 \ndegree of agreement between metrics on the direction of divide migration and the impact of asymmetry magnitude on that \nagreement. 80 2.2 Tectonics and Lithology Alternative models for the deformation and uplift of the HDM include \n120 \ncrustal thickening (Kirby and Ouimet, 2011) and lower crustal flow (Royden et al., 1997; Clark et al., 2004, 2006). The role \nof early topography and river reorganization due to the accretion of the Burma terrane is also potentially important, but not \nwell investigated (Westerweel et al., 2019). The complex tectonic history of the HDM has resulted in a heterogeneous surfa The complex tectonic history of the HDM has resulted in a heterogeneous surface geology. Bedrock in the region \npredominantly consists of sedimentary formations, ranging from Paleozoic to Eocene in age, and Pre-Mesozoic metamorphic \n125 \nrocks, along with some Mesozoic and Cenozoic granites (China Geological Survey Bureau, 2019). Neogene and Quaternary \ndeposits are found in many sedimentary basins and low-relief river catchments (e.g., Yanyuan Basin, Anning River). predominantly consists of sedimentary formations, ranging from Paleozoic to Eocene in age, and Pre-Mesozoic metamorphic \n125 \nrocks, along with some Mesozoic and Cenozoic granites (China Geological Survey Bureau, 2019). Neogene and Quaternary \ndeposits are found in many sedimentary basins and low-relief river catchments (e.g., Yanyuan Basin, Anning River). https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 2.2 Tectonics and Lithology The HDM are framed by the rigid crustal block of the Sichuan Basin to the east and the Burma Block to the southwest (Wang \n110 \net al., 2017a). The region is dominated by large NE-SW trending strike-slip faults (Fig. 1a), with dextral faults on its southwest \nmargin (e.g., Red River Fault) and sinistral faults on its northeast margin (e.g., Xianshuihe-Xiaojiang fault system). Faults \nwith a predominantly thrust component, including the Yalong Thrust Belt, run roughly perpendicular in a SW-NE orientation, \nin line with the neighboring Longmen Shan fault system. Active movement is also documented on normal faults in the HDM, including a 6.6 Mw earthquake near the First Bend of the Yangtze River in 1996 (Ji et al., 2017). 115 \nThe HDM has experienced active uplift and faulting since at least the Eocene (Su et al., 2019) with additional localized \nincreases in uplift rates observed in the Plio-Pleistocene (Li et al., 2018). The region’s strike-slip faults are thought to \naccommodate part of the tectonic strain from the ongoing India-Eurasia collision through the extrusion and rotation of material \nfrom the Tibetan Plateau around the Eastern Himalayan syntaxis and through the HDM (Tapponnier et al., 1982; England and including a 6.6 Mw earthquake near the First Bend of the Yangtze River in 1996 (Ji et al., 2017). 115 \nThe HDM has experienced active uplift and faulting since at least the Eocene (Su et al., 2019) with additional localized \nincreases in uplift rates observed in the Plio-Pleistocene (Li et al., 2018). The region’s strike-slip faults are thought to \naccommodate part of the tectonic strain from the ongoing India-Eurasia collision through the extrusion and rotation of material \nfrom the Tibetan Plateau around the Eastern Himalayan syntaxis and through the HDM (Tapponnier et al., 1982; England and Houseman, 1986; Leloup et al., 1995; Tian et al., 2014). Alternative models for the deformation and uplift of the HDM include \n120 \ncrustal thickening (Kirby and Ouimet, 2011) and lower crustal flow (Royden et al., 1997; Clark et al., 2004, 2006). The role \nof early topography and river reorganization due to the accretion of the Burma terrane is also potentially important, but not \nwell investigated (Westerweel et al., 2019). Houseman, 1986; Leloup et al., 1995; Tian et al., 2014). 2.1 Drainage Network River capture has significantly altered the drainage network \n100 \nin the HDM (Clark et al., 2004), however the timing and drivers of landscape transience are still debated, with hypotheses \nincluding the onset of regional surface uplift and strike-slip faulting in the Eocene (Clark et al., 2004; Gourbet et al., 2017), an \nintensified monsoon in the Miocene (Zheng et al., 2021), volcanic activity (Gourbet et al., 2017), and/or an increase in localized \nuplift rate in the Plio-Pleistocene (Yang et al., 2020). Documented major captures include the rerouting of the upper Yangtze from the paleo-Red River sometime between the Eocene and Pleistocene (Kong et al., 2012; Sun et al., 2020; Liu et al., 2020; \n105 \nZheng et al., 2021) and the diversion of the upper Dadu from its previous path through the Anning River in the Pleistocene \n(Yang et al., 2020). Many rivers in the HDM align with fault traces for long stretches (e.g., Red, Yalong, and Anning rivers), \nmaking the drainage network particularly susceptible to tectonic activity. 4 4 3.1 Data Sources All geomorphic analyses were performed in MATLAB using TopoToolbox (Schwanghart and Scherler, 2014) and the \nCopernicus digital elevation model (DEM) with 90 m spatial resolution (The European Space Agency, 2021). Mean annual \n145 \nprecipitation data for precipitation corrections was obtained at 1 km resolution from the CHELSA global climate model (Karger \net al., 2017, 2018). Scientific colormaps used in the figures are from Crameri (2023). Copernicus digital elevation model (DEM) with 90 m spatial resolution (The European Space Agency, 2021). Mean annual \n145 \nprecipitation data for precipitation corrections was obtained at 1 km resolution from the CHELSA global climate model (Karger \net al., 2017, 2018). Scientific colormaps used in the figures are from Crameri (2023). https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. landscape features (Fu et al., 2013; Zhang et al., 2016). Glaciers persist in some of the high elevation (>5,000 m) portions of \n140 \nthe HDM, particularly in the Three Rivers area and Daxue Shan (Wang et al., 2017b). landscape features (Fu et al., 2013; Zhang et al., 2016). Glaciers persist in some of the high elevation (>5,000 m) portions of \n140 \nthe HDM, particularly in the Three Rivers area and Daxue Shan (Wang et al., 2017b). 2.3 Climate The HDM are largely temperate in climate, transitioning to subtropical in the south and becoming increasingly cold and arid \nin the northwest as they merge into the Tibetan Plateau (Kottek et al., 2006). Precipitation in the HDM varies seasonally due \n130 \nto its position at the nexus of the Asian Monsoon (both South Asian and East Asian) and the Westerlies, which drive wet \nsummers and dry winters, respectively (Wang et al., 2010). Annual precipitation in the region varies by over an order of \nmagnitude, ranging from approximately 180-3,260 mm/a, with most precipitation concentrated in the west due to a \ncombination of the South Asian Monsoon and topographic effects (Bookhagen and Burbank, 2010; Karger et al., 2017, 2018; Fig. 1c). Another, weaker summer precipitation high in the eastern HDM is controlled by the East Asian Monsoon. Monsoon \n135 \nonset is debated, but the region has experienced a strong monsoonal influence since ~20 Ma (Betzler et al., 2016; Tada et al., \n2016). Fig. 1c). Another, weaker summer precipitation high in the eastern HDM is controlled by the East Asian Monsoon. Monsoon \n135 \nonset is debated, but the region has experienced a strong monsoonal influence since ~20 Ma (Betzler et al., 2016; Tada et al., \n2016). During the last glacial maximum ~20 ka, glaciers in the HDM extended below at least 4,000 m (Fu et al., 2013). Residual evidence of Pleistocene glaciations is found throughout the HDM in the form of moraines, glacial lakes, and other 5 landscape features (Fu et al., 2013; Zhang et al., 2016). Glaciers persist in some of the high elevation (>5,000 m) portions of \n140 \nthe HDM, particularly in the Three Rivers area and Daxue Shan (Wang et al., 2017b). 3.2 Geomorphic Metrics Cross-divide differences were determined for four metrics--catchment-restricted local relie Cross-divide differences were determined for four metrics--catchment-restricted local relief (CRR), hillslope gradient (HSG), Cross-divide differences were determined for four metrics--catchment-restricted local relief (CRR), hillslope gradient (HSG), \nχ, and normalized stream channel steepness (ksn) across the entire HDM region. These metrics have been suggested to predict \n150 \ndrainage divide migration direction in various tectonic and climatic settings (Willett et al., 2014; Whipple et al., 2017; \nWinterberg and Willett, 2019). Catchment-restricted local relief (CRR) is the maximum elevation difference within a fixed radius window, where \nthe averaging window does not consider points on opposing sides of a specified drainage divide. CRR thus prevents the blurring of relief across drainage divides, which is important when evaluating cross-divide differences. CRR was calculated for the \n155 \nregion with a radius of 5 km, similar to the methods of Winterberg & Willett (2019). The stream network was derived and \ncatchment outlets and confluences were defined using TopoToolbox for all streams with a minimum drainage area of 10 km2. For every confluence or outlet, the upstream local relief was calculated only within the catchment using the divides to the next \ncatchment as a boundary. Where there is overlap, the catchment with the lower Strahler stream order is used to produce a composite map. The CRR MATLAB script is publicly available on Zenodo (Gelwick and Ott, 2023a). For comparison, \n160 \ntraditional local relief (LR) was also calculated with a radius of 5 km (Fig. S1). Hillslope gradient (HSG) was calculated using the gradient8 function in TopoToolbox, which determines the steepest \ndownward gradient of a DEM pixel using an eight-connected neighborhood. composite map. The CRR MATLAB script is publicly available on Zenodo (Gelwick and Ott, 2023a). For comparison, \n160 \ntraditional local relief (LR) was also calculated with a radius of 5 km (Fig. S1). Hill l\ndi\nt (HSG)\nl\nl t d\ni\nth\ndi\n8 f\nti\ni T\nT\nlb\nhi h d t\ni\nth\nt\nt Hillslope gradient (HSG) was calculated using the gradient8 function in TopoToolbox, which determines the steepest \ndownward gradient of a DEM pixel using an eight-connected neighborhood. predict landscape evolution (e.g., Wobus et al., 2006). Subsequent work argues that the map pattern of χ can be used to estimate geometric \nequilibrium or disequilibrium and to infer drainage divide movement in order to achieve geometric equilibrium (Willett et al., \n2014). Assuming spatially invariant U and K and integrating Eq. 3, yields: \n𝑧(𝑥) = 𝑧(𝑥𝑏) + 𝑘𝑠𝑛𝐴0\n−𝜃𝑟𝑒𝑓𝜒 \n \n \n \n \n \n \n \n \n \n (5a) 185 (5a) and \n190 and \n190 𝜒= ∫(\n𝐴0\n𝐴(𝑥′))\n𝜃\n𝑑𝑥′\n𝑥\n𝑥𝑏 (5b) where xb is the stream base-level, and A0 is a reference scaling area (Perron and Royden, 2013). A base-level of 500 m was \nused for the study area to approximate the elevation at the western edge of the Sichuan Basin, which serves as a natural base-\nlevel for most streams in the HDM via the Yangtze River (Fig. 1a). As with ksn, we applied a precipitation correction to χ to \nget χP such that: \n195 where xb is the stream base-level, and A0 is a reference scaling area (Perron and Royden, 2013). A base-level of 500 m was \nused for the study area to approximate the elevation at the western edge of the Sichuan Basin, which serves as a natural base-\nlevel for most streams in the HDM via the Yangtze River (Fig. 1a). As with ksn, we applied a precipitation correction to χ to \nget χP such that: \n195 𝜒𝑃= ∫(\n𝐴0\n𝐴(𝑥′)𝑃)\n𝜃\n𝑑𝑥′\n𝑥\n𝑥𝑏\n \n \n \n \n \n \n \n \n \n \n \n (6) 𝜒𝑃= ∫(\n𝐴0\n𝐴(𝑥′)𝑃)\n𝜃\n𝑑𝑥′\n𝑥\n𝑥𝑏 𝜒𝑃= ∫(\n𝐴0\n𝐴(𝑥′)𝑃)\n𝜃\n𝑑𝑥′\n𝑥\n𝑥𝑏\n \n \n \n \n \n \n \n \n \n \n \n (6) 𝜒𝑃= ∫(\n𝐴0\n𝐴(𝑥′)𝑃)\n𝜃\n𝑑𝑥′\n𝑥\n𝑥𝑏 (6) Results of χ without the precipitation correction can be found in the supplement (Fig. S1). Results of χ without the precipitation correction can be found in the supplement (Fig. S1). https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. landscape is achieved by adjusting channel slope until E = U (dz/dt=0). At steady-state, local channel slope can be expressed \n170 landscape is achieved by adjusting channel slope until E = U (dz/dt=0). At steady-state\n170 landscape is achieved by adjusting channel slope until E = U (dz/dt=0). At steady-state, local channel slope can be expressed \n70 \nas: as: \n𝑆= 𝑘𝑠𝐴−𝜃 \n \n \n \n \n \n \n \n \n \n \n \n (2) \nwhere ks is channel steepness corrected for drainage area and θ is stream concavity, which is equivalent to m/n (Hack, 1957; \nFlint, 1974). Fixing θ to a reference value (θref) enables the comparison of normalized channel steepness (ksn) across a given 𝑆= 𝑘𝑠𝐴−𝜃 (2) region (Wobus et al., 2006): \n175 \n𝑆= 𝑘𝑠𝑛𝐴−𝜃𝑟𝑒𝑓 \n \n \n \n \n \n \n \n \n \n \n \n (3) \nksn can then be used to infer variation in rock uplift rates of steady-state streams, assuming little or no variation in K (similar \nclimate and lithology) in the study region. A best-fit θref of 0.45 was determined for the HDM through Bayesian optimization \nwith the mnoptim function in Topotoolbox (Fig. S2). Given that the strong precipitation gradient in the HDM region could (3) ksn can then be used to infer variation in rock uplift rates of steady-state streams, assuming little or no variation in K (similar \nclimate and lithology) in the study region. A best-fit θref of 0.45 was determined for the HDM through Bayesian optimization \nwith the mnoptim function in Topotoolbox (Fig. S2). Given that the strong precipitation gradient in the HDM region could 180 alter the drainage area discharge scaling, we applied a precipitation correction to ksn to get ksnP. Following the methods of \n180 \nAdams et al. (2020), we multiply A by mean annual precipitation (P) for an improved stream discharge proxy: \n𝑘𝑠𝑛𝑃= (𝐴𝑃)𝜃𝑟𝑒𝑓𝑆 \n \n \n \n \n \n \n \n \n \n \n (4) (4) See supplement for results of ksn without the precipitation correction (Fig. S1). For all calculations, a critical drainage \narea threshold of 5 km2 was used. See supplement for results of ksn without the precipitation correction (Fig. S1). For all calculations, a critical drainage \narea threshold of 5 km2 was used. 185 Chi (χ) is the horizontal transformation of distance along a stream and was originally developed to reduce noise in \n185 \nstream profiles (Perron and Royden, 2013). 3.2 Geomorphic Metrics Combining the stream power incision model with conservation of mass, \n165 \nstream elevation profiles can be described by: \n𝑑𝑧𝑑𝑡\n⁄\n= 𝑈−𝐸= 𝑈−𝐾𝐴𝑚𝑆𝑛 \n \n \n \n \n \n \n \n \n \n (1) \nwhere U is rock uplift rate, E is erosion rate, K is an erodibility constant, A is drainage area, S is channel slope, and m and n \nare empirical scaling factors (Howard, 1994; Whipple and Tucker, 1999). Over time, given constant U and K, a steady-state 165 (1) where U is rock uplift rate, E is erosion rate, K is an erodibility constant, A is drainage area, S is channel slope, and m and n \nare empirical scaling factors (Howard, 1994; Whipple and Tucker, 1999). Over time, given constant U and K, a steady-state 6 cipitation correction can be found in the supplement (Fig. S1). 3.3 Divide asymmetry Drainage divides for the HDM were determined using the DIVIDEobj function in Topotoolbox (Scherler and Schwanghart, \n2020). This generates a divide network, similar to a stream network, where divides can be ordered. Divide segments are \n200 7 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. separated from each other by drainage junctions. For every stream pixel, the mean of all upslope pixels was computed for each \ngeomorphic metric (upslopestats function) and then projected to the drainage divides (mapfromnal function). Divide \nasymmetry was then calculated for each geomorphic metric using a modified version of the asymmetry function in Topotoolbox \n(Scherler and Schwanghart, 2020), where the median of all pixels along the divide was calculated on either side of each divide segment before determining the asymmetry of the segment. This buffers outliers and double-counting of pixels in paired pixel \n205 \ncomparisons of the original function. The asymmetry function was also modified to ensure that the direction of asymmetry is \nalways perpendicular to the average orientation of the divide segment, which is important for comparison between geomorphic \nmetrics. The magnitude of divide asymmetry was quantified using a modified version of the divide asymmetry index (DAI) \nproposed by Scherler and Schwanghart (2020): segment before determining the asymmetry of the segment. This buffers outliers and double-counting of pixels in paired pixel \n205 \ncomparisons of the original function. The asymmetry function was also modified to ensure that the direction of asymmetry is \nalways perpendicular to the average orientation of the divide segment, which is important for comparison between geomorphic \nmetrics. The magnitude of divide asymmetry was quantified using a modified version of the divide asymmetry index (DAI) \nproposed by Scherler and Schwanghart (2020): 205 𝐷𝐴𝐼= |\n∆𝜇\n∑𝜇| \n \n \n \n \n \n \n \n \n \n \n \n (7) \n210 \nwhere μ is the mean value of a given geomorphic metric on either side of a divide segment. By normalizing the across-divide \ndifferences by their sum, DAI allows for a simple comparison of asymmetry magnitudes within and across geomorphic metrics. DAI ranges between 0 and 1, for completely symmetric and maximally asymmetric divides, respectively. The MATLAB script \nwe used to calculate DAI for all of the metrics is publicly available on Zenodo (Gelwick and Ott, 2023b). (7) For the purpose of this study, after calculating DAI for each geomorphic metric across the HDM, we restricted our \n215 \nanalysis to divides of Strahler order four or greater (Scherler and Schwanghart, 2020). Excluding minor divides reduces noise \nfrom small nested catchments and allows us to focus on the signal of large-scale geometric change in the HDM. 4.1 Divide asymmetry by geomorphic metrics There is strong spatial variation in all measured divide asymmetry metrics across the HDM (Fig. 2). CRR is generally low \n220 \nabove 29° N, towards the Tibetan Plateau, and below 26° N (Fig. 2a). Exceptions to this pattern include especially high relief \naround the Eastern Himalayan syntaxis and in the Longmen Shan region. Between 26° N and 29° N, CRR is generally high, \nespecially in the Three Rivers Area and Daxue Shan. Several patches of low relief occur within this latitude range and include \nthe Li River catchment, Yanyuan Basin, Anning River catchment, and upper Heng River catchment (numbered 3-6 in Fig. 2d). Above 29° N, a few additional areas with low relief are surrounded by regions of higher relief, including the Yuqu River \n225 \ncatchment and Liqiu River catchment (numbered 1 and 2, respectively in Fig. 2d). Variations in HSG closely mirror spatial \npatterns in CRR, especially in low relief areas (Fig. 2b). Values of ksnP are highest in the Three Rivers and around the Eastern \nHimalayan syntaxis (Fig. 2c). There are stronger contrasts in these areas in the ksnP map compared to the ksn map (Fig. S1). Otherwise, variations in ksnP closely mimic those described in CRR and HSG. As χP increases with distance from base-level, Above 29° N, a few additional areas with low relief are surrounded by regions of higher relief, including the Yuqu River \n225 \ncatchment and Liqiu River catchment (numbered 1 and 2, respectively in Fig. 2d). Variations in HSG closely mirror spatial \npatterns in CRR, especially in low relief areas (Fig. 2b). Values of ksnP are highest in the Three Rivers and around the Eastern \nHimalayan syntaxis (Fig. 2c). There are stronger contrasts in these areas in the ksnP map compared to the ksn map (Fig. S1). Otherwise, variations in ksnP closely mimic those described in CRR and HSG. As χP increases with distance from base-level, Above 29° N, a few additional areas with low relief are surrounded by regions of higher relief, including the Yuqu River \n225 \ncatchment and Liqiu River catchment (numbered 1 and 2, respectively in Fig. 2d). Variations in HSG closely mirror spatial \npatterns in CRR, especially in low relief areas (Fig. 2b). Values of ksnP are highest in the Three Rivers and around the Eastern \nHimalayan syntaxis (Fig. 2c). There are stronger contrasts in these areas in the ksnP map compared to the ksn map (Fig. S1). 4.1 Divide asymmetry by geomorphic metrics Otherwise, variations in ksnP closely mimic those described in CRR and HSG. As χP increases with distance from base-level, it is highest in the upper reaches of major rivers in the north (Fig. 2d). However, strong contrasts in χP are observed at catchment \n230 \nboundaries throughout the study region, including around the previously mentioned low-relief features. it is highest in the upper reaches of major rivers in the north (Fig. 2d). However, strong contrasts in χP are observed at catchment \n230 \nboundaries throughout the study region, including around the previously mentioned low-relief features. 8 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 2: Geomorphic metrics across the HDM and corresponding divide asymmetry. Panels show CRR (a), HSG (b), ksnP (c), and \nχP (d). White lines are drainage divides, with thicker lines indicating a higher divide asymmetry index (DAI) for the specific metric. Black arrows show inferred divide migration direction; arrow length does not scale with DAI. Numbers in (d) correspond to low-\nrelief landscape features discussed in the text: (1) Yuqu River catchment, (2) Liqiu River catchment, (3) Li River catchment, (4) \nYanyuan Basin, (5) Anning River catchment, and (6) upper Heng River catchment. As the DAI ranges are distinctly different between geomorphic metrics (Fig. 3, Table S1), we assign a separate \nthreshold for “high,” and “low” divide asymmetry in each metric. We define highly asymmetric divides as having DAI values Figure 2: Geomorphic metrics across the HDM and corresponding divide asymmetry. Panels show CRR (a), HSG (b), ksnP (c), and \nχP (d). White lines are drainage divides, with thicker lines indicating a higher divide asymmetry index (DAI) for the specific metric. Black arrows show inferred divide migration direction; arrow length does not scale with DAI. Numbers in (d) correspond to low-\n235 \nrelief landscape features discussed in the text: (1) Yuqu River catchment, (2) Liqiu River catchment, (3) Li River catchment, (4) \nYanyuan Basin, (5) Anning River catchment, and (6) upper Heng River catchment. Figure 2: Geomorphic metrics across the HDM and corresponding divide asymmetry. Panels show CRR (a), HSG (b), ksnP (c), and \nχP (d). White lines are drainage divides, with thicker lines indicating a higher divide asymmetry index (DAI) for the specific metric. Black arrows show inferred divide migration direction; arrow length does not scale with DAI. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. low asymmetry. DAI statistics for each metric, including the high and low asymmetry thresholds, can be found in \nSupplementary Table S1. χP have a higher concentration of highly asymmetric divides to the south and east, possibly because these areas have the lowest \n245 \nelevations in the HDM. χ and χP have less variation in DAI along continuous divides compared to the other metrics because χ \nis integrated at the scale of entire catchments. Drainage divides with low asymmetry are also distributed relatively uniformly \nin all geomorphic metrics, except for χ and χP, in which fewer are found to the south and east (Fig. S2). Comparing distributions of high and low drainage divides between CRR, HSG, and ksnP, reveals that 2,915 unique \ndivide segments have high asymmetry and 14,727 unique segments have low asymmetry in at least one metric (Fig. S3). These \n250 \ngroupings overlap in 1,396 divide segments, meaning that 6% of all drainage divides are classed as having both high and low \nasymmetry in different metrics. There is no clear spatial pattern of divides with contradicting classifications (Fig. S3). Highly asymmetric drainage divides occur in both high- and low-relief areas (Table S2). All geomorphic metrics \nshow high divide asymmetry in the previously identified high- (Eastern Himalayan syntaxis, Three Rivers area, Daxue Shan) \nand low-relief areas (Fig. 2d), except in the Anning River catchment, where only χ and χP have high asymmetry (Table S2). 255 4.1 Divide asymmetry by geomorphic metrics Numbers in (d) correspond to low-\n235 \nrelief landscape features discussed in the text: (1) Yuqu River catchment, (2) Liqiu River catchment, (3) Li River catchment, (4) \nYanyuan Basin, (5) Anning River catchment, and (6) upper Heng River catchment. As the DAI ranges are distinctly different between geomorphic metrics (Fig. 3, Table S1), we assign a separate \nthreshold for “high,” and “low” divide asymmetry in each metric. We define highly asymmetric divides as having DAI values \nin the 95th percentile for the specific metric. Divide segments with DAI values in the 5th percentile are designated as having \n240 As the DAI ranges are distinctly different between geomorphic metrics (Fig. 3, Table S1), we assign a separate \nthreshold for “high,” and “low” divide asymmetry in each metric. We define highly asymmetric divides as having DAI values \nin the 95th percentile for the specific metric. Divide segments with DAI values in the 5th percentile are designated as having \n240 9 4.2 Agreement in divide asymmetry direction and magnitude between geomorphic metrics Inferred divide migration directions agree between geomorphic metrics for a majority of the 22,837 divide segments analyzed. Closely related metrics, i.e., catchment-restricted and local relief, ksn and ksnP, χ and χP, agree on migration direction very \nstrongly with each other with 95.4%, 97.0%, and 94.9% of divide segments agreeing on migration direction between CRR and local relief, ksnP and ksn, and χP and χ, respectively (Fig. 3). CRR agrees with other metrics on divide migration direction more \n260 \noften than any other metric, with agreement ranging between 64.5% (χP) and 71.3% (ksn) of divide segments. χP and χ agree \nleast often with other metrics, the lowest total agreement being with HSG at 57.0% and 57.2% of divide segments for χP and \nχ, respectively. The geomorphic metrics infer an inward divide migration direction around all of the previously described low-\nrelief landscape features (Fig. 2d), except at the headwaters of the Anning River catchment, where only χ and χP consistently infer an inward migration direction. 265 \nDespite overall good agreement in divide migration direction across metrics, the magnitude of divide asymmetry varies \nsignificantly by metric. DAI distributions are right-skewed in all metrics, with a majority of divide segments having a low \nDAI, however, distributions vary (Fig. 3). The metrics ksnP, ksn, and CRR have the broadest DAI distributions (ranging from 0 \nto 1), however ksnP and ksn are less skewed than any of the other metrics (Fig. 3, Fig. S3). All other metrics have maximum infer an inward migration direction. 265 \nDespite overall good agreement in divide migration direction across metrics, the magnitude of divide asymmetry varies \nsignificantly by metric. DAI distributions are right-skewed in all metrics, with a majority of divide segments having a low \nDAI, however, distributions vary (Fig. 3). The metrics ksnP, ksn, and CRR have the broadest DAI distributions (ranging from 0 \nto 1), however ksnP and ksn are less skewed than any of the other metrics (Fig. 3, Fig. S3). All other metrics have maximum g\nDespite overall good agreement in divide migration direction across metrics, the magnitude of divide asymmetry varies \nsignificantly by metric. DAI distributions are right-skewed in all metrics, with a majority of divide segments having a low \nDAI, however, distributions vary (Fig. 3). 4.2 Agreement in divide asymmetry direction and magnitude between geomorphic metrics The metrics ksnP, ksn, and CRR have the broadest DAI distributions (ranging from 0 \nto 1), however ksnP and ksn are less skewed than any of the other metrics (Fig. 3, Fig. S3). All other metrics have maximum \nDAI values of less than 0 9 with fewer than 20 divide segments exceeding a DAI of 0 7 in any single metric\n270 DAI values of less than 0.9, with fewer than 20 divide segments exceeding a DAI of 0.7 in any single metric. 270 \nDivide segments with higher magnitudes of asymmetry are more likely to agree on migration direction across multiple \nmetrics. Figure 3 shows a strong positive correlation between increasing DAI and percent agreement on divide migration 10 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. direction in all metrics. For instance, CRR shows more than 75% agreement in migration direction for all metrics if the DAI \nis > 0.2. However, this relationship becomes noisy at high DAI values for which most metrics lack a sufficient sample size. direction in all metrics. For instance, CRR shows more than 75% agreement in migration direction for all metrics if the DAI \nis > 0.2. However, this relationship becomes noisy at high DAI values for which most metrics lack a sufficient sample size. direction in all metrics. For instance, CRR shows more than 75% agreement in migration direction for all metrics if the DAI \nis > 0.2. However, this relationship becomes noisy at high DAI values for which most metrics lack a sufficient sample size. direction in all metrics. For instance, CRR shows more than 75% agreement in migration direction for all metrics if the DAI \nis > 0.2. However, this relationship becomes noisy at high DAI values for which most metrics lack a sufficient sample size. 5 \nFigure 3: Plots of percent agreement in divide migration direction between chosen metrics and all calculated metrics (points), binned \nby corresponding divide asymmetry index (DAI) for indicated metric in intervals of 0.05. Grey histograms show the distributions of \nDAI values in log-scale for each metric. Higher DAI corresponds with increased agreement in migration direction between metrics. Histograms show variability in DAI distributions in different metrics. 275 Figure 3: Plots of percent agreement in divide migration direction between chosen metrics and all calculated metrics (points), binned \nby corresponding divide asymmetry index (DAI) for indicated metric in intervals of 0.05. Grey histograms show the distributions of \nDAI values in log-scale for each metric. Higher DAI corresponds with increased agreement in migration direction between metrics. Histograms show variability in DAI distributions in different metrics. 280 Except between closely related metrics (i.e., CRR/LR, ksnP/ksn, χP/χ), the Pearson correlation coefficient of DAI values is \n280 \n≤0.30 for all metric comparisons (Fig. 4). Consistent with divide migration direction agreement, the highest Pearson correlation \ncoefficients correspond with CRR, with values ranging between 0.21 to 0.30 (Fig. 4). Despite a high Pearson correlation \ncoefficient between CRR and LR of 0.63, correlation coefficient values for local relief with other metrics are consistently the \nlowest with values not exceeding 0.19 (Fig. 4). 11 285 \nFigure 4: Pearson’s coefficient of variation of divide asymmetry index (DAI) between each pair of metrics (bold, black values). Blue \ncircles show distributions of DAI for each comparison with linear fit indicated by black lines. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 285 Figure 4: Pearson’s coefficient of variation of divide asymmetry index (DAI) between each pair of metrics (bold, black values). Blue \ncircles show distributions of DAI for each comparison with linear fit indicated by black lines. 4.3 Comparison between integral and local-scale geomorphic metrics There is a fundamental difference between the local-scale metrics of divide asymmetry (LR, C integral quantities (χ, χP) that complicates the interpretation of disagreement between them. The integral quantities are \n290 \nmeasures of geometric disequilibrium within a catchment but do not necessarily relate directly to the instantaneous or current \nasymmetry of a specific divide segment. Furthermore, they can be affected by spatial variations in uplift rate or channel bed \nerodibility occurring anywhere downstream of the assessment point. The integral metrics can thus reflect transient processes \nin distal parts of the catchment, rather than processes local to a divide. Agreement between the local and integral quantities indicates that spatial variations in uplift rate or erodibility are unlikely and that internal transients, due to processes such as \n295 \nriver capture have propagated upstream to reach the divides resulting in a quasi-stable state (Beeson et al., 2017; Adams et al., \n2020). χP agrees with a majority (2+) of the other geomorphic metrics for 69.2% of the 22,837 divide segments analyzed in \nthe HDM. As shown in Figure 3, χP is more likely to predict a contrary divide migration direction when it, or the metric it is \nbeing compared to, has a low DAI. When only divide segments for which all four metrics, including χP, have a minimum DAI of 0.05 are counted (n=3,068), χP agrees with at least two of the three other geomorphic metrics 87.2% of the time. At a DAI \n300 \nthreshold of 0.1 (n=537), the agreement increases to 92.9%. Disagreements between χP and the other metrics are found across \nthe entire HDM region, but certain regions have higher concentrations of disagreement (e.g., Eastern Himalayan syntaxis, \nboundary between Dadu River and Anning River catchments, southwest and southeast margins of study area; Fig. 5). 12 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 5: Map of χP (background) with lines representing drainage divides. Divides are colored by agreement/disagreement in \n305 \nmigration direction between χP and a majority of selected geomorphic metrics (i.e., CRR, HSG, ksnP). White divides indicate \nagreement between χP and at 2+ other metrics. Red divides indicate disagreement between χP and 2+ other metrics. Divide line \nthickness corresponds to DAI of χP, with thicker divides having greater asymmetry. 4.3 Comparison between integral and local-scale geomorphic metrics Black numbered circles indicate low-relief \ncatchments identified as undergoing drainage area-loss feedback: (1) Yuqu River catchment, (2) Liqiu River catchment, (3) Li River \ncatchment, (4) Yanyuan Basin, (5) Anning River catchment, and (6) upper Heng River catchment. 310 Figure 5: Map of χP (background) with lines representing drainage divides. Divides are colored by agreement/disagreement in \n305 \nmigration direction between χP and a majority of selected geomorphic metrics (i.e., CRR, HSG, ksnP). White divides indicate \nagreement between χP and at 2+ other metrics. Red divides indicate disagreement between χP and 2+ other metrics. Divide line \nthickness corresponds to DAI of χP, with thicker divides having greater asymmetry. Black numbered circles indicate low-relief \ncatchments identified as undergoing drainage area-loss feedback: (1) Yuqu River catchment, (2) Liqiu River catchment, (3) Li River \ncatchment, (4) Yanyuan Basin, (5) Anning River catchment, and (6) upper Heng River catchment. 310 305 2d and 5) exhibit good agreement \nbetween χP and local metrics, despite the area’s glacial history (Zhang et al., 2016). Additional reported drivers of cross-divide \nerosional differences include river capture (Willett et al., 2014; Beeson et al., 2017; Scheingross et al., 2020), non-uniform bedrock erodibility (Gallen, 2018; Wang et al., 2023; Mitchell and Forte, 2023), tectonic advection (Chen et al., 2021; He et \n340 \nal., 2021; Mitchell and Forte, 2023), landsliding (Dahlquist et al., 2018), and autogenic fluvial processes (Scheingross et al., \n2020). The discordance in divide migration direction between χP and local metrics in weakly asymmetric divides suggests that \nmigration direction is more susceptible to minor shifts in erosion rates on one side of the divide, such as those caused by \nlithologic differences or autogenic fluvial processes, when the drainage network is geometrically balanced. bedrock erodibility (Gallen, 2018; Wang et al., 2023; Mitchell and Forte, 2023), tectonic advection (Chen et al., 2021; He et \n340 \nal., 2021; Mitchell and Forte, 2023), landsliding (Dahlquist et al., 2018), and autogenic fluvial processes (Scheingross et al., \n2020). The discordance in divide migration direction between χP and local metrics in weakly asymmetric divides suggests that \nmigration direction is more susceptible to minor shifts in erosion rates on one side of the divide, such as those caused by \nlithologic differences or autogenic fluvial processes, when the drainage network is geometrically balanced. Our results highlight the importance of the concept of quasi-topographic equilibrium when interpreting divide \n345 \nmigration metrics, in which landscape transience can be characterized by a combination of long-term adjustments towards a \nquasi-equilibrium state and short-term perturbations driven by local geometry and transient mechanisms (Beeson et al., 2017; \nAdams et al., 2020). Although we find evidence for local tectonic and geomorphic perturbations driving local divide migration, \nthe drainage network is dominated by quasi-equilibrium conditions in which most river profile perturbations have reached the \nwater divide and are driving steady migration of the divides and asymmetry in all indicators\n350 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Montgomery, 2001), especially in tectonically active areas like the HDM. Channel steepness (ksn) also exhibits non-linear and \npotentially threshold behaviour (Hilley et al., 2019). This is consistent with the metric-dependent ranges and distributions of \nDAI (Table S1, Fig. 3). Therefore, identical DAI values in different metrics would likely be associated with different divide \nmigration rates. Due to this potential for threshold behavior, we use metric-specific thresholds to distinguish drainage divides \nwith high and low asymmetry. We consider drainage divides in the 95th and 5th percentiles of each geomorphic metric to have \nhigh and low asymmetry, respectively (Table S1, Fig. 3). 325 Contradicting predictions for divide migration direction between metrics may be the result of local-scale metrics (i.e., \nCRR, HSG, ksnP) responding to variations in rock uplift and erodibility. This is supported by high concentrations of \ndisagreement between χP and local-scale metrics in areas with known localized uplift (e.g., Eastern Himalayan Syntaxis, Contradicting predictions for divide migration direction between metrics may be the result of local-scale metrics (i.e., \nCRR, HSG, ksnP) responding to variations in rock uplift and erodibility. This is supported by high concentrations of \ndisagreement between χP and local-scale metrics in areas with known localized uplift (e.g., Eastern Himalayan Syntaxis, \nLongmen Shan; Fig. 5). However, divide migration direction inferred by χP still agrees with local metrics a majority of the \n330 \ntime, especially in strongly asymmetrical divides. This suggests that, despite several areas of localized uplift and heterogeneous Longmen Shan; Fig. 5). However, divide migration direction inferred by χP still agrees with local metrics a majority of the \n330 \ntime, especially in strongly asymmetrical divides. This suggests that, despite several areas of localized uplift and heterogeneous \nlithology across the HDM, changes in uplift and erodibility are small enough, or slow enough, that χP values at the divides \nremain dominated by the length of the rivers and their area distributions in most places. Other processes can further complicate the divide asymmetry relationship between geomorphic metrics with different p\np\ny\ny\np\ng\np\nspatial and temporal scales. For example, glaciers, which are concentrated along drainage divides, could have altered catchment \n335 \nrelief and slope through planation, over-deepening, and/or asymmetrical erosion (Lai and Huppert, 2023). Interestingly, \nhowever, drainage divides of the low-relief Yiqui River catchment (labelled “2” in Figs. 5.1 Comparison of geomorphic metrics and implications in the HDM Overall, geomorphic metrics agree on divide migration direction in a majority of cases (Figs. 3 and 5). Where geomorphic \nmetrics contradict each other, divide asymmetry is typically low, indicating a more stable divide (Fig. 3). The variation in DAI \nacross metrics for any given divide segment is the result of the relative spatial scales and sensitivity of each metric. Metrics \n315 \nare computed on different spatial and, consequently, temporal scales; for example, χP is integrated across entire catchments \nand represents long-term trends in landscape evolution (Beeson et al., 2017; Scheingross et al., 2020), while HSG is averaged \nacross individual divide segments and adjusts to local, short-term changes to catchment structure. Overall, geomorphic metrics agree on divide migration direction in a majority of cases (Fig\nmetrics contradict each other, divide asymmetry is typically low, indicating a more stable divid metrics contradict each other, divide asymmetry is typically low, indicating a more stable divide (Fig. 3). The variation in DAI \nacross metrics for any given divide segment is the result of the relative spatial scales and sensitivity of each metric. Metrics \n315 \nare computed on different spatial and, consequently, temporal scales; for example, χP is integrated across entire catchments \nand represents long-term trends in landscape evolution (Beeson et al., 2017; Scheingross et al., 2020), while HSG is averaged \nacross individual divide segments and adjusts to local, short-term changes to catchment structure. Additionally, it should be noted that some metrics may have distinct threshold behavior. For instance, it has been \nargued that HSG and relief reach a threshold above which they do not correlate with erosion rate (Burbank et al., 1996; \n320 Additionally, it should be noted that some metrics may have distinct threshold behavior. For instance, it has been \nargued that HSG and relief reach a threshold above which they do not correlate with erosion rate (Burbank et al., 1996; \n320 13 5.2 Continental-scale landscape transience in the HDM Except in the case of χP, there is no clear spatial pattern in DAI magnitudes in the HDM, with high and low values distributed \nrelatively uniformly (Fig. S2). The greater incidence of divides with high χP asymmetry in the east and south may be due to 14 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. these areas having lower elevations. The existence of strongly asymmetrical drainage divides throughout the HDM combined \nwith the lack of spatial trends is evidence of landscape transience at a continental scale. 55 these areas having lower elevations. The existence of strongly asymmetrical drainage divides throughout the HDM combined \nwith the lack of spatial trends is evidence of landscape transience at a continental scale. 355 \nWhile DAI has not been calibrated to absolute rates of drainage divide migration for any metric, divides with strong \nasymmetries may migrate relatively quickly. For instance, divides with a ksnP DAI of 0.564, our threshold for high ksnP \nasymmetry, would have a 56.4% difference in cross-divide erosion rates based on the Stream Power Law (Eq. 1), assuming a \nlinear relationship between channel steepness and erosion rates (where n = 1). 355 While DAI has not been calibrated to absolute rates of drainage divide migration for any metric, divides with strong \nasymmetries may migrate relatively quickly. For instance, divides with a ksnP DAI of 0.564, our threshold for high ksnP \nasymmetry, would have a 56.4% difference in cross-divide erosion rates based on the Stream Power Law (Eq. 1), assuming a \nlinear relationship between channel steepness and erosion rates (where n = 1). Catchments surrounded by highly asymmetric divides migrating inward are particularly suggestive of active drainage \n360 \nreorganization, with the bounded catchments losing drainage area to their neighbors. This pattern was identified and interpreted \nby Yang et al. (2015) as representative of catchments which have lost headwaters to a large river capture event and are \nsubsequently collapsing due to their reduced erosional power, in what has been referred to as an area-loss feedback (Willett et \nal., 2014). Many examples in the region, including the Yuqu River catchment, Liqiu River catchment, Li River catchment, Yanyuan Basin, and upper Heng River catchment (numbered in Figs. 2d and 5), fit this model well. 365 \nHowever, in areas in the HDM without any clear captures or where local-scale metrics contradict χP at catchment \nboundaries, the source of transience is unclear. In these cases, a local analysis of landscape features is required and additional \ndrivers, such as those discussed in the previous section, should be considered. 5.3 Influence of discrete river capture on short-term divide asymmetry \n370 Discrete river captures can temporarily alter divide asymmetry relationships while the stream channels and corresponding \nhillslopes adjust to the change in erosive power (Prince et al., 2010, 2011; Willett et al., 2014; Whipple et al., 2017). Notably, \nχP predicts a migration direction contradictory to the other metrics across much of the drainage boundary between the Dadu \nand Anning rivers (Fig. 5), where a major river capture recently occurred and ongoing divide migration is well-documented (Yang et al., 2020). 375 \nWe expect that the discrepancy is a direct consequence of the planform adjustments occurring to the river network \nfollowing capture of the former upper Anning to the modern Dadu ~2 Ma (Yang et al., 2020). The modern upper Dadu is \nsuggested to have been captured at the location of the “initial windgap” circled in Fig. 6. The capture event resulted in the \nformation of a new drainage divide just south of the capture point that has since migrated to the south by approximately 40 km. This divide migration has reversed the river along the former path of the Anning and led to the capture of the adjacent \n380 \ntributaries in a pattern characteristic of headward extension of the Dadu tributary (Bishop, 1995; Harel et al., 2022; Fig. 6a). A consequence of this process is that much of the modern drainage divide between the Anning and the Dadu formed as a divide \nfully internal to the Anning catchment and has only recently become the main divide between the Anning and the Dadu through \ncapture of a lateral tributary (Fig. 7a, “pre-lateral capture divide”). km. This divide migration has reversed the river along the former path of the Anning and led to the capture of the adjacent \n380 \ntributaries in a pattern characteristic of headward extension of the Dadu tributary (Bishop, 1995; Harel et al., 2022; Fig. 6a). A consequence of this process is that much of the modern drainage divide between the Anning and the Dadu formed as a divide \nfully internal to the Anning catchment and has only recently become the main divide between the Anning and the Dadu through \ncapture of a lateral tributary (Fig. 7a, “pre-lateral capture divide”). km. Below, we include one such analysis from the \nAnning River catchment in which we examine how discrete river capture can temporarily alter drainage divide asymmetry. Yanyuan Basin, and upper Heng River catchment (numbered in Figs. 2d and 5), fit this model well. 365 \nHowever, in areas in the HDM without any clear captures or where local-scale metrics contradict χP at catchment \nboundaries, the source of transience is unclear. In these cases, a local analysis of landscape features is required and additional \ndrivers, such as those discussed in the previous section, should be considered. Below, we include one such analysis from the \nAnning River catchment in which we examine how discrete river capture can temporarily alter drainage divide asymmetry. 5.3 Influence of discrete river capture on short-term divide asymmetry \n370 \nDiscrete river captures can temporarily alter divide asymmetry relationships while the stream channels and corresponding \nhillslopes adjust to the change in erosive power (Prince et al., 2010, 2011; Willett et al., 2014; Whipple et al., 2017). Notably, \nχP predicts a migration direction contradictory to the other metrics across much of the drainage boundary between the Dadu \nand Anning rivers (Fig. 5), where a major river capture recently occurred and ongoing divide migration is well-documented 5.3 Influence of discrete river capture on short-term divide asymmetry \n370 This divide migration has reversed the river along the former path of the Anning and led to the capture of the adjacent \n380 \ntributaries in a pattern characteristic of headward extension of the Dadu tributary (Bishop, 1995; Harel et al., 2022; Fig. 6a). A consequence of this process is that much of the modern drainage divide between the Anning and the Dadu formed as a divide \nfully internal to the Anning catchment and has only recently become the main divide between the Anning and the Dadu through \ncapture of a lateral tributary (Fig. 7a, “pre-lateral capture divide”). 385 15 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 6: (a) Schematic of drainage capture between the modern Dadu and Paleo-Dadu-Anning rivers. Blue lines show current flow \npaths of Dadu and Anning rivers; orange line shows the river reach that has been reversed since the initial capture; dark grey lines \nare former tributaries to the Anning that have been captured by the Dadu; the main drainage divides are outlined in black. Blue \narrows show modern flow direction, orange arrow shows pre-capture flow direction. Initial capture point (initial windgap) and \n0 \nmodern windgap are indicated by black circles. Grey shading shows approximate modern extent of the Daxue Mountains. (b) Map \nof CRR and drainage divides (white and red lines). Red divides indicate disagreement between χP and 2+ other metrics. Divide line \nthickness corresponds to DAI of CRR, with thicker divides having greater asymmetry. Black arrows show inferred divide migration \ndirection based on CRR. (c) Map of HSG and drainage divides (white and red lines). Red divides indicate disagreement between χP \nand 2+ other metrics. Divide line thickness corresponds to DAI of HSG. Black arrows show inferred divide migration direction \n5 \nbased on HSG. (d) Map of ksnP and drainage divides (white and red lines). Red divides indicate disagreement between χP and 2+ \nother metrics. Divide line thickness corresponds to DAI of ksnP. Black arrows show inferred divide migration direction based on ksnP. c⃝Author(s) 2023. CC BY 4.0 License. Figure 6: (a) Schematic of drainage capture between the modern Dadu and Paleo-Dadu-Anning rivers. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. To support this model of headward expansion of the Dadu, we examine the stream profiles of opposing side tributaries \nfrom this segment of the main Dadu-Anning drainage divide, starting from the approximate elevation of the Anning River \noutlet at 1,000 m (Fig. 7). A clear change in channel steepness is observed in the Dadu stream profile, with a low-gradient \n400 \nupstream segment that begins above a dam that was constructed ca. 2013 and likely corresponds with a natural knickpoint \n(Fig. 7b). The knickpoint also coincides with where disagreement between χP and local-scale metrics begins along the Dadu-\nAnning drainage divide (Fig. 7a). No significant change in channel steepness is observed in the Anning profile, despite also \ncrossing a dammed section (Fig, 7b). Notably, the flowpaths of the opposing tributaries’ main trunks coincide with, and are \nlik l\nt\nll d b\nb\nh f th A\ni\nh F\nlt\n405 405 Figure 7: (a) CRR map from Fig. 6b. Black lines highlight section of the Dadu (1) and Anning (2) tributaries shown as opposing \nstream profiles in b. Remaining stream network is shown in grey. White “X” marks the knickpoint below the portion of the Dadu \ntributary recently acquired through lateral river capture. Drainage divides are shown in red (χP disagrees with CRR) and white (χP \nagrees with CRR). Divide line thickness corresponds to DAI of CRR. Location of the main, modern windgap between the Dadu and \n10 \nAnning catchments (same as in Fig. 6a) is indicated by the black circle. The “pre-lateral capture divide” was the drainage boundary \nbetween the Dadu and Anning catchments before the recent lateral river capture. White dotted line outlines the portion of the pre-\ncapture divide not included in the analysis due to low Strahler order. Note that CRR is markedly lower on what was the Anning side \nof the pre-capture divide and predicts the same migration direction as χP. (b) Opposing stream profiles (χ vs. elevation) \ncorresponding to segments of Dadu and Anning rivers shown in a, initiating from the white star labeled “minor windgap.” For \n15 \nplotting purposes, χ baselevel is set at 1000 m (approx. elevation of Anning outlet). Abrupt reduction in slope (mean ksn of 41 vs. 327) \nabove the knickpoint (white “X”) in the Dadu profile corresponds with the recent lateral river capture from the Anning tributary \nshown in profile 2. Grey profiles are side branches of the recently captured tributary which have not been reversed. Prior to capture, \nthe stream segments above the knickpoint flowed into the Anning. Figure 7: (a) CRR map from Fig. 6b. Black lines highlight section of the Dadu (1) and Anning (2) tributaries shown as opposing \nstream profiles in b. Remaining stream network is shown in grey. White “X” marks the knickpoint below the portion of the Dadu \ntributary recently acquired through lateral river capture. Drainage divides are shown in red (χP disagrees with CRR) and white (χP \nagrees with CRR). Divide line thickness corresponds to DAI of CRR. Location of the main, modern windgap between the Dadu and \n410 \nAnning catchments (same as in Fig. 6a) is indicated by the black circle. The “pre-lateral capture divide” was the drainage boundary \nbetween the Dadu and Anning catchments before the recent lateral river capture. White dotted line outlines the portion of the pre-\ncapture divide not included in the analysis due to low Strahler order. Note that CRR is markedly lower on what was the Anning side \nof the pre-capture divide and predicts the same migration direction as χP. (b) Opposing stream profiles (χ vs. elevation) \ncorresponding to segments of Dadu and Anning rivers shown in a, initiating from the white star labeled “minor windgap.” For \n415 \nplotting purposes, χ baselevel is set at 1000 m (approx. elevation of Anning outlet). Abrupt reduction in slope (mean ksn of 41 vs. 327) \nabove the knickpoint (white “X”) in the Dadu profile corresponds with the recent lateral river capture from the Anning tributary \nshown in profile 2. Grey profiles are side branches of the recently captured tributary which have not been reversed. Prior to capture, \nthe stream segments above the knickpoint flowed into the Anning. The Dadu knickpoint is concurrent with the lateral tributary’s outlet, suggesting that the low-gradient tributary was captured \n420 \nby the Dadu, having formerly flowed into the Anning via the corresponding windgap (Fig. 7a, “minor windgap”). The initial \ncapture of the tributary led to a flow reversal along its main trunk (black line above dam outlet in Fig. 7) and the capture of \nadditional tributary branches (solid grey lines above dam outlet in Fig. 7). 5.3 Influence of discrete river capture on short-term divide asymmetry \n370 Bl\npaths of Dadu and Anning rivers; orange line shows the river reach that has been reversed since the initia\nare former tributaries to the Anning that have been captured by the Dadu; the main drainage divides a\narrows show modern flow direction, orange arrow shows pre-capture flow direction. Initial capture po\n0 \nmodern windgap are indicated by black circles. Grey shading shows approximate modern extent of the D\nof CRR and drainage divides (white and red lines). Red divides indicate disagreement between χP and 2+\nthickness corresponds to DAI of CRR, with thicker divides having greater asymmetry. Black arrows show\ndirection based on CRR. (c) Map of HSG and drainage divides (white and red lines). Red divides indicate\nand 2+ other metrics. Divide line thickness corresponds to DAI of HSG. Black arrows show inferred d\n5 \nbased on HSG. (d) Map of ksnP and drainage divides (white and red lines). Red divides indicate disagre\nother metrics. Divide line thickness corresponds to DAI of ksnP. Black arrows show inferred divide migrati Figure 6: (a) Schematic of drainage capture between the modern Dadu and Paleo-Dadu-Anning rivers. Blue lines show current flow \npaths of Dadu and Anning rivers; orange line shows the river reach that has been reversed since the initial capture; dark grey lines \nare former tributaries to the Anning that have been captured by the Dadu; the main drainage divides are outlined in black. Blue \narrows show modern flow direction, orange arrow shows pre-capture flow direction. Initial capture point (initial windgap) and \nmodern windgap are indicated by black circles. Grey shading shows approximate modern extent of the Daxue Mountains. (b) Map \nof CRR and drainage divides (white and red lines). Red divides indicate disagreement between χP and 2+ other metrics. Divide line \nthickness corresponds to DAI of CRR, with thicker divides having greater asymmetry. Black arrows show inferred divide migration \ndirection based on CRR. (c) Map of HSG and drainage divides (white and red lines). Red divides indicate disagreement between χP \nand 2+ other metrics. Divide line thickness corresponds to DAI of HSG. Black arrows show inferred divide migration direction \nbased on HSG. (d) Map of ksnP and drainage divides (white and red lines). Red divides indicate disagreement between χP and 2+ \nother metrics. Divide line thickness corresponds to DAI of ksnP. Black arrows show inferred divide migration direction based on ksnP. 16 https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Whipple and Tucker, 1999). Immediately following a river capture event, the diverted stream segment and corresponding \n425 \nhillslopes will not yet have adjusted to the new base-level. Local-scale geomorphic metrics will predict a false migration \ndirection until the kinematic wave has propagated through the entire captured reach. Thus, the apparent disagreement between \nχP and local-scale metrics at the Dadu-Anning divide should only persist for a short time, while χP continues to predict the \ncorrect long-term migration direction. This interpretation is supported by the agreement between χP and local-scale metrics on \ndivide migration direction in what would have been the Dadu-Anning drainage divide pre-capture (Fig. 7a). 430 divide migration direction in what would have been the Dadu-Anning drainage divide pre-capture (Fig. 7a). 430 6 Conclusions This is \nexemplified by the Dadu-Anning drainage divide scenario where χP’s asymmetry contrasts with the other metrics, despite \naccurately forecasting the Anning River’s ongoing capture by the Dadu River. In addition to delineating geometrically transient and stable areas in the HDM landscape at high resolution, our work \ndemonstrates the important differences in local and regional predictors of transience. We show how disagreement across \n445 \ntimescales can be used as evidence for discrete transient events, such as river capture, whereas consensus amongst all indicators \nis evidence for a state of quasi-equilibrium in which geometric change occurs in the drainage network in a near-steady fashion. These insights are broadly applicable to the study of landscape transience and drainage divide asymmetry in other regions, \nregardless of their tectonic, geologic, or climatic setting. demonstrates the important differences in local and regional predictors of transience. We show how disagreement across \n445 \ntimescales can be used as evidence for discrete transient events, such as river capture, whereas consensus amongst all indicators \nis evidence for a state of quasi-equilibrium in which geometric change occurs in the drainage network in a near-steady fashion. These insights are broadly applicable to the study of landscape transience and drainage divide asymmetry in other regions, \nregardless of their tectonic, geologic, or climatic setting. River capture triggers a kinematic wave with a \nresponse time determined by the erosive power of the expanded catchment (Weissel and Seidl, 1998; Seidl et al., 1992; 17 6 Conclusions We investigated drainage divide asymmetry using four geomorphic steepness or elevation metrics (CRR, HSG, ksnP, and χP) \nto understand the spatial and temporal patterns of geometric transience in the HDM river network. We find clear evidence of \nwidespread transience through a high incidence of strongly asymmetrical divides throughout the HDM in all geomorphic \nmetrics. 435 435 Our findings demonstrate that χP, which is a proxy for the long-term landscape evolution, generally agrees with local-\nscale geomorphic metrics that are only sensitive to local erosional processes, especially in areas of pronounced asymmetry. This suggests that most of the landscape is in a quasi-equilibrium state where short-term and long-term disequilibrium indicate \nthe same sense of geometric change in the drainage network. It also suggests that variations in climate, lithology, and uplift \nrate are small compared to the normalized length of rivers. However, disruptions to long-term trends in landscape evolution, \n440 \nas indicated by discrepancies between χP and local metrics, can arise from localized tectonic or geomorphic events. This is \nexemplified by the Dadu-Anning drainage divide scenario where χP’s asymmetry contrasts with the other metrics, despite \naccurately forecasting the Anning River’s ongoing capture by the Dadu River Our findings demonstrate that χP, which is a proxy for the long-term landscape evolution, generally agrees with local-\nscale geomorphic metrics that are only sensitive to local erosional processes, especially in areas of pronounced asymmetry. This suggests that most of the landscape is in a quasi-equilibrium state where short-term and long-term disequilibrium indicate \nthe same sense of geometric change in the drainage network. It also suggests that variations in climate, lithology, and uplift \nrate are small compared to the normalized length of rivers. However, disruptions to long-term trends in landscape evolution, \n440 rate are small compared to the normalized length of rivers. However, disruptions to long-term trends in landscape evolution, \n440 \nas indicated by discrepancies between χP and local metrics, can arise from localized tectonic or geomorphic events. This is \nexemplified by the Dadu-Anning drainage divide scenario where χP’s asymmetry contrasts with the other metrics, despite \naccurately forecasting the Anning River’s ongoing capture by the Dadu River. rate are small compared to the normalized length of rivers. However, disruptions to long-term trends in landscape evolution, \n440 \nas indicated by discrepancies between χP and local metrics, can arise from localized tectonic or geomorphic events. https://doi.org/10.5194/egusphere-2023-2303\nPreprint. Discussion started: 10 November 2023\nc⃝Author(s) 2023. CC BY 4.0 License. References Adams, B. A., Whipple, K. X., Forte, A. M., Heimsath, A. M., and Hodges, K. V.: Climate controls on erosion in tectonically \n465 \nactive landscapes, Science Advances, 6, eaaz3166, https://doi.org/10.1126/sciadv.aaz3166, 2020. Beeson, H. W., McCoy, S. W., and Keen-Zebert, A.: Geometric disequilibrium of river basins produces long-lived transient \nlandscapes, Earth and Planetary Science Letters, 475, 34–43, https://doi.org/10.1016/j.epsl.2017.07.010, 2017. Betzler, C., Eberli, G. P., Kroon, D., Wright, J. D., Swart, P. K., Nath, B. N., Alvarez-Zarikian, C. A., Alonso-García, M., Adams, B. A., Whipple, K. X., Forte, A. M., Heimsath, A. M., and Hodges, K. 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H.: Drainage basin dynamics during the transition from early to mature \norogeny in Southern Taiwan, Earth and Planetary Science Letters, 562, 116874, https://doi.org/10.1016/j.epsl.2021.116874, \n2021. Competing interests The authors declare that they have no conflict of interest. The authors declare that they have no conflict of interest. Acknowledgements \n460 \nThis research was supported by the Swiss National Science Foundation (grant no. 189846) and the National Natural Science \nFoundation of China (grant no. 41961134031). This manuscript benefited from conversations with Richard F. Ott, Helen W. Dow, and Kimberly L. Huppert. We also thank Ott for his MATLAB assistance. Code and data availability \n450 All MATLAB scripts used in this study are available online at: https://doi.org/10.5281/zenodo.8363611 and \nhttps://doi.org/10.5281/zenodo.8416264. 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