Relationship Between Dams, Knickpoints and the Longitudinal Profile of the Upper Indus River

Mass movements in mountainous areas are capable of damming rivers and can have a lasting effect on the river longitudinal profile. The long profile is commonly used to retrieve regional tectonic information, but how much dams may compromise geomorphometry-based tectonic analysis has not been systematically researched. In this study, we investigate the relationship between river dams and the longitudinal profile of the upper Indus River basin, based on interpretation and analysis of remote sensing imagery and digital elevation models (DEMs) and local field work. We identified 178 landslide, glacier and debris flow dams. Using TopoToolbox, we automatically extracted the river longitudinal profile from the 30 m SRTM DEM, determined the location of convex knickpoints and calculated the channel steepness index. One hundred and two knickpoints were detected with heights above 148 m, of which 55 were related to dams. There is good spatial correspondence between dams, convexities in the river longitudinal profile and relatively high steepness index. Different dam types have different impacts on the river profile; on the upper Indus, debris flow dams have a greater impact than landslide and glacier dams and can form knickpoints of up to 900 m. Therefore, dams may have a significant influence on the river longitudinal profile, knickpoints and steepness index, and should be considered when extracting information on regional tectonics using these indices.

Introduction

Rivers are not only a significant driving force of geomorphic evolution, but also an important geomorphic unit that can record other driving forces such as tectonic activity and climate change (Whipple, 2000; Beaumont et al., 2001; Kirby and Whipple, 2001; Kirby et al., 2003; Whipple, 2004; Zhang et al., 2017). The development of high-resolution and global digital elevation applications has facilitated extraction and analysis of the river longitudinal profile and they have been widely used to derive indexes of long-term tectonic evolution (Hu et al., 2010; Pánek et al., 2010; Goren et al., 2014; Willett et al., 2014; Yang et al., 2015; Wang et al., 2017). The long profile can act as a predictor of zones of erosion and sediment deposition during extreme events (Korup, 2006a; Korup, 2006b; Korup and Montgomery, 2008; Walsh et al., 2012) and reflect the repeated impact of mass-wasting events (Korup, 2006b). River longitudinal profiles and metrics derived from them, such as channel steepness indexes and knickpoints (Wobus et al., 2006), have become critical tools for studying the topographic evolution of mountain belts and deciphering changes in climate and tectonics (Bishop et al., 2005). The most direct and widely observed expression of river adjustment to transient or intrinsic perturbations is a discrete change in river gradient, commonly termed a “knickpoint”. As the number and spatial distribution of knickpoints and knickzones play an important role in interpreting tectonically active landscapes, it is critical that studies use a reproducible method of quantifying their locations (Gailleton et al., 2019).

In recent years, studies have shown that the river longitudinal profile may exhibit a significant response to extreme damming events (Korup, 2006a; Korup, 2006b; Korup and Montgomery, 2008; Korup et al., 2010a; Walsh et al., 2012). However, the role of natural dams is often not considered when extracting quantitative morphological parameters from the long profile; knickpoints caused by extreme events may compromise the reliability of tectonic and climate change interpretations. The Nanga Parbat–Haramosh Massif in the western Himalayas comprises some of the greatest relief on Earth, with the highest measured rates of uplift, denudation and river incision in bedrock (Hewitt et al., 2011). Many studies have sought to understand how the morphology of the massif relates to tectonics, glaciation and sediment yield (Shroder et al., 1993; Korup et al., 2007), but few extreme damming events had been recognized and many of their impacts had been attributed to other processes (Hewitt, 2009a).

Existing research on dams in the Himalayan region is mostly concentrated in the southeastern margin of the Qinghai-Tibetan Plateau (Ouimet et al., 2007; Xu et al., 2013; Chen and Cui, 2015; Liu et al., 2015; Liu et al., 2018). Using remote sensing interpretation and field survey, Korup et al. (2010b) identified over 900 glacier and landslide dams along the Yarlung Tsangpo and Indus rivers that were consistent with the distribution of river knickpoints; the study concluded that the damming effect played an important role in maintaining the integrity of the Qinghai-Tibetan Plateau. The Indus River is mainly located in the western Himalayan tectonic syntaxes, with deep valleys and numerous glaciers. Hewitt (2009a) identified nearly 150 landslide dams in the upper Indus River, and suggested that Holocene river system evolution has been controlled by landslide-driven channel deposition and incision, rather than tectonic activity and glaciation. Hewitt (2009a) concluded that the contribution of extreme damming events to denudation and sedimentation in the upper Indus River and its tributaries had been largely overlooked.

Many previous studies have used river profiles and knickpoints to retrieve information on tectonic activity (Hu et al., 2010; Goren et al., 2014), but they seldom consider the long-term impact of extreme surface processes on fluvial geomorphology (Korup, 2006a; Ouimet et al., 2007). A few studies have confirmed the correlation between dams and knickpoints, and suggested causal linkages (Korup, 2006b; Walsh et al., 2012; Scheingross et al., 2020), but not examined the influence of different dam types on the magnitude of river knickpoints. In this preliminary study, we identify different types of dams in the upper Indus River basin and quantify their influence on the river longitudinal profile and knickpoint magnitude, in order to understand the geomorphic response of river damming. The study provides basic data for evaluating mechanisms driving the geomorphic evolution of the western Himalayan syntaxes.

Study Area

The upper Indus River in the southwestern Qinghai-Tibetan Plateau, is part of the western Himalayan syntaxes, which covers the Hindu Kush, Karakoram and most of the western Himalayan mountains (Immerzeel et al., 2015) (Figure 1). There are large differences in local climate with altitude, from arid valley floors at lower elevations to perennial ice climates with heavy snowfall and extensive glacier cover at higher elevations (Hewitt, 1994). Glaciers transport large volumes of debris and their melt waters dominate the flow of the upper Indus. Earthquakes and extreme weather events combine with rugged, steep terrain to produce countless large landslides, snow avalanches, rock falls and debris flows; and there is a history of catastrophic floods from the breaching of natural dams (Hewitt, 1982).

FIGURE 1

FIGURE 1. Location of the study area in the upper Indus River, western Himalayas.

The upper Indus basin has a subtropical climate, with a strong monsoon that is affected by the regional topography to give a large precipitation gradient (Figure 2). A global precipitation dataset (http://www.worldclim.org/) indicates mean annual precipitation in the catchment from 1970 to 2000 of c. 25–1,353 mm/a (Figure 2A) the variation trend of precipitation distribution is inconsistent with the variation of channel stepness (k_sn), so precipitation will not be taken as the factor affecting river channel variation in this paper. A partial geological map of the upper Indus, mainly covering the Indus gorge and downstream reaches (http://www.ngac.org.cn), shows complex lithology in the downstream areas comprising intrusive and effusive volcanic rock, metamorphic rocks and sedimentary rocks (Figure 2B). The area is mainly controlled by two fault zones, the Indus Suture and Shyok Suture. The Shyok Suture Zone, mainly represented in ophiolites, traverses the Indus River. The Indus Suture forms the rapidly uplifting Nanga Parbat–Haramosh Massif, which mainly comprises lower Neoproterozoic strata. Quaternary uplift rates in the Indus gorge are estimated at 3–10 mm/a, with the higher rates on the Massif (Zeitler, 1985; Whittington et al., 1999). However, high rates of bedrock incision in the Indus gorge of 3–12 mm/a suggest fluvial down-cutting is able to keep pace with uplift (Burbank et al., 1996). This means that deposits are well preserved despite high rates of tectonic uplift and erosion in the Karakoram Himalaya.

FIGURE 2

FIGURE 2. Precipitation and geology of the study area. (A) Normalized channel steepness (k_sn) and average annual precipitation (1970–2000) in the upper Indus River (Precipitation data are c. 1 km² resolution, from http://www.worldclim.org/version2): (B) Regional geological map (resolution 1:1,000,000, from http://www.ngac.org.cn).

Materials and Methods

Remote Sensing Interpretation of Dams

In remote areas such as the upper Indus, dams can be identified using a range of data sources, including literature review, field survey and remote sensing interpretation, combined with multi-period, high-resolution Google Earth images. Since we can only conduct field surveys on the China-Pakistan highway section, and other places cannot be reached, there is currently no better method to obtain data on dams for those places that cannot go to field surveys, therefore, the dams are mainly obtained through remote sensing interpretation in this study. The location of the dam was delineated using a combination of features including location of residual dam materials on both sides of the river channel, variation in river channel width, and presence of lacustrine sediments upstream channel or outburst flood sediment downstream (Figure 3).

FIGURE 3

FIGURE 3. Example of remote sensing interpretation of dams from high-resolution Google Earth images. (A) Landslide; (B) Glacier dam; (C) Debris flow; (D) Glacier dam; (E) Outburst flood sediment; (F) lacustrine sediments.

The source or cause of the dam can be assessed using remote sensing interpretation; the three most common river-blocking mechanisms are landslide, debris flow and glacier advance, so identification of dam type is mainly based on the recognition of these features (Figure 3). At present, the remote sensing interpretation signs of these three types of mass movement have been relatively clearly studied (Li et al., 2015; Xu et al., 2014; Fan et al., 2019a; Bazai et al., 2021). This research uses unified remote sensing interpretation signs (e.g. morphology, tone, vegetation, texture structure and so on) to identify different mass movement (Table 1).

TABLE 1

TABLE 1. Remote sensing interpretation signs of different mass movement.

In this study, we first established a set of remote sensing interpretation criteria for large landslides, debris flows and glaciers and applied it to visual interpretation of Google Earth images covering the upper Indus River (Table 1; Figure 3). Second, we collated results of studies on the Indus River to obtain the location of previously researched landslides and glaciers (Hewitt, 1998; Hewitt, 2009b; Hewitt, 2011). We use these data to verify and to revise the results of remote sensing interpretation. The height of the dam was determined using the difference in elevation between the highest point of the dam and the valley bottom by the Google Earth images. The geomorphic features we used to delineate dams represent the effect of long-term process, so the dams are paleo-dams that are the product of ancient landslide, debris flow and glacier activity, rather than contemporary features.

Digital Terrain Analysis

We obtained 30-m-resolution SRTM DEM data from National Aeronautics and Space Administration (NASA, https://earthdata.nasa.gov). We used ArcGIS to preprocess the DEM to obtain the complete Indus River basin, with the raster projection converted to WGS 1984_UTM 43 projection coordinates. Then we used TopoToolbox (Schwanghart and Scherler, 2014; https://topotoolbox.wordpress.com/) to extract river geomorphic parameters from the projected DEM, as outlined below.

Smoothing the River Longitudinal Profile

The analysis of river longitudinal profiles is an important tool for studying landscape evolution, however, characterizing river profiles based on digital elevation models (DEMs) is prone to errors and artifacts, particularly along valley bottoms, and elevations are commonly overestimated in steep topography. To avoid these problems, we used the constrained regularized smoothing (CRS) algorithm of Schwanghart and Scherler (2017) to correct and smooth the bumpy river profile (Figure 4A). CRS relies on quantile regression to enable hydrological correction and to quantify uncertainty on river profiles; the method uses quantile carving to reconstruct the profile along different quantiles, rather than using the minima and maxima of the commonly-used carving and filling approach (Figure 4B) (Schwanghart and Scherler, 2017). The CRS approach reduces elevation bias and errors in longitudinal river profiles compared with the conventional carving and filling method (Schwanghart and Scherler, 2017). Another advantage of the CRS method is that it can be applied to the whole river network, so facilitates a systematic analysis of the Indus River profile. In applying the CRS method to the Indus basin, we set the smoothing parameter K to five and quantile τ to 0.5.

FIGURE 4

FIGURE 4. Schematic diagrams illustrating the method of smoothing the river profile using the approach of Schwanghart and Scherler (2017). (A) Smoothing using the constrained regularized smoothing (CRS) method; (B) Quantile carving of the longitudinal profile.

Extraction of River Knickpoints

Changes in river profile steepness or abrupt vertical steps in channels are thought to be indicative of changes in erosion rates, lithology or other factors that affect landscape evolution (Gailleton et al., 2019). These changes are termed knickpoints or knickzones and are widespread in bedrock river systems. The number and spatial distribution of knickpoints has been widely used in studies of tectonically active landscapes (Bishop et al., 2005). A range of different methods have been adopted to quantify knickpoint locations, which hinders comparisons, and commonly-used slope–area approaches make pinpointing of knickpoint locations difficult (Kirby and Whipple, 2012). We applied the KnickpointFinder function in TopoToolbox, which reproducibly extracts knickpoint locations from smooth river profiles, over the whole river network (Schwanghart and Scherler, 2014; Schwanghart and Scherler, 2017). The function has fewer parameters and is computationally more efficient than other methods (Gailleton et al., 2019) and the code is readily available online (https://topotoolbox.wordpress.com/).

The KnickpointFinder function uses an algorithm that adjusts a strictly concave upward profile to the actual profile. Offsets between the actual and the concave upward profile occur where the actual profile has convexities. Relaxing the concavity constraint where offsets attain a maximum will adjust the concave profile to the actual profile. KnickpointFinder adjusts the profile iteratively until offsets fall below a specified tolerance value (reflecting uncertainties inherent in river longitudinal profile data). Tolerance values selected should be higher than the maximum expected error between the measured and the true river profile. With lower tolerance values we would likely increase the false positive rate, i.e., the probability of choosing a knickpoint that is due to an artifact (Schwanghart and Scherler, 2017). To exclude knickpoints caused by artifacts, only those with heights greater than the tolerance value are selected to ensure the minimum number of real knickpoints are obtained. Using Schwanghart and Scherler, (2017) algorithm to calculate the research area river network, the tolerance parameter in TopoToolbox was fixed to 148 to extract river knickpoints. We used knickpoint height (e.g., derived from comparison with a reference profile) to directly quantify knickpoint magnitude in this paper.

Using the Stream-Power River Incision Model to Determine Steepness Index

In the stream-power river incision model, the elevation change of the channel is expressed by the difference between the uplift rate (U) of the bedrock and the erosion rate (E) of the river (Whipple, 2004):

$\frac{\text{dz}}{\text{dt}}=U\left(x,t\right)-E(1)$

Where, z represents the elevation of the river, x is distance from the estuary and t is time. The erosion rate of the river is given by the functional relationship between catchment area (A) and slope (S):

$E=K\cdot A^{\text{m}}\cdot S^n(2)$

Where, K represents the erosion coefficient, and m and n are indexes of catchment area and slope, respectively. Combining Eqs. 1, 2:

$\frac{d\text{z}}{d\text{t}}=U\left(x,t\right)-K{A}^m{\left(\frac{dz}{dx}\right)}^n(3)$

$S=\frac{dz}{dx}$ is river slope. When local morphology is in equilibrium the channel elevation does not change with time and the bedrock uplift rate is in balance with the river erosion rate, so $\frac{dz}{dt}=0$, giving the steady state stream-power river incision equation:

$\frac{\text{dz}}{\text{dx}}={\left(\frac{U}{K}\right)}^{\frac{1}{n}}\cdot A^{\frac{-m}{n}}(4)$

Rearranging Equation 4 by $k_s={\left(\frac{U}{K}\right)}^{\frac{1}{n}}$, river concavity $\theta =\raisebox{1ex}{$m$}\!\left/ \!\raisebox{-1ex}{$n$}\right.$ and $S=\frac{dz}{dx}$, the slope–area relationship gives:

$S=k_s{A}^{-\theta }(5)$

In this study, we calculated the river gradient and basin area based on the smooth river profile obtained using the CRS algorithm (Schwanghart and Scherler, 2017). We determined the standardized river steepness index (k_sn) when θ = 0.45, and derived the average k_sn for each 1000-m section of the river.

Results

Distribution of Dams and Steepness Index

The visual remote sensing interpretation identified 178 dams in the upper Indus region (Figure 5). Eighty-four landslide dams were located, mainly distributed in the middle and lower reaches of the main stream of the Indus River, 61 debris flow dams, mainly in the two major tributaries of the upper Shyork and middle Gilgit rivers, and 33 glacier dams, mainly in the upper reaches of the Hunza River. Overall, the distribution of the steepness index k_sn seems to be influenced by topography and faults, with low values in the interior of the plateau and larger values at the edge of the plateau. The highest k_sn is on the Nanga Parbat–Haramosh Massif which is characterized by the highest rates of erosion and uplift in the region. Comparing the distribution of dams with k_sn and faults (Figure 5), shows that some dams are adjacent to fault lines and most are in areas with high k_sn. Analysis of the height and elevation characteristics of the dams identified in the upper Indus (Table 2) shows that over 60% of dams are 100–300 m in height. In term of elevation, most dams are found in middle and high altitude areas, between 2000 and 4,000 m.

FIGURE 5

FIGURE 5. Spatial distribution of dams in the upper Indus River as identified from remote sensing interpretation.

TABLE 2

TABLE 2. Height and elevation characteristics of natural dams in the upper Indus River.

Extraction of River Geomorphic Parameters

Overall River Network

Figure 6 plots the results of the basin-wide knickpoint extraction using the TopoToolbox (Schwanghart and Scherler, 2014, 2017). Knickpoints are expressed as the difference to an idealized concave up profile; they are mainly found in the middle and lower reaches and small tributaries of the upper Indus River, while they are largely absent from upper reaches within the plateau (Figure 6). Figure 7 maps knickpoint locations with faults and k_sn. Knickpoints mainly correspond with higher k_sn and partly with fault lines. Large-magnitude knickpoints are mainly found in the plateau and alpine valley transition zone, the Nanga Parbat-Halamush Massif and Indus gorge.

FIGURE 6

FIGURE 6. River longitudinal profile and location of river knickpoints in the upper Indus River.

FIGURE 7

FIGURE 7. Distribution of knickpoints and normalized channel steepness (k_sn) in the river network of the upper Indus River.

Preliminary analysis of the height and elevation characteristics of knickpoints (Table 3) shows that the largest proportion are between 148–200 m in height and are located in the upper valley area of the river. Knickpoints between 200–300 m in height are found in small tributaries and glacial valleys on both sides of the main stream. Knickpoints between 300–400 m in height are located near the fault zone and those between 300–400 m in height are near the Indus gorge. Only five knickpoints are above 500 m in height, and most of these in the plateau-alpine canyon transition zone. Over three quarter of knickpoints are in the 148–300 m height range, and most are at 2000–4,000 m elevation, which is a similar distribution to the dams. This may indicate that mass movement sediments are more concentrated in middle and low mountain areas (2000–4,000 m).

TABLE 3

TABLE 3. Height and elevation characteristics of knickpoints in the upper Indus River.

Main Stream Knickpoints

DEM artifacts in tributary valleys make the overall tolerance values for knickpoint extraction larger compared to the main stream Indus River, which confounds direct comparison. To reduce the effect of the DEMs artifacts and increase the accuracy of knickpoint extraction, we divided the Indus basin into the main stream and seven sub-basins: Shyok River, Shigar River, Hunza River, Gilgit River, Astor River, Shingo River, and Zanskar River (Figure 8). The quantile carving method was used to calculate the tolerance value for the main steam in each sub-basin, then knickpoints were identified through iterative calculation using the KnickpointFinder function, giving a total of 30 knickpoints (Table 4). Figure 8 shows that the two knickpoints with height >400 m are in Indus River and Shyok Rive, mainly in the plateau region transition zone. Further comparison showed that the value of k_sn at the two knickpoints also changed significantly. In Indus River upstream of the knickpoint, the value of k_sn was relatively small with an average of 44 m^0.9, while in the downstream, the value of k_sn increased significantly with an average of 293 m^0.9. In Shyok River upstream of the knickpoint, the value of k_sn was relatively small with an average of 40 m^0.9, while in the downstream, the value of k_sn increased significantly with an average of 276 m^0.9.

FIGURE 8

FIGURE 8. Distribution of knickpoints and normalized channel steepness (k_sn) in the main stream and sub-basins of the upper Indus River.

TABLE 4

TABLE 4. Tolerance values and knickpoint extraction results for the main stream of major sub-basins in the Indus River basin (see Figure 8 for locations).

Discussion

Effects of Damming on Channel

The relationship between the river longitudinal profile and dams in the eight sub-basins of the upper Indus is plotted in Figure 9. We found that dams have a great influence on the longitudinal profile and steepness index of the river, and the three types of dam have different effects. On the Indus River, debris flow dams correspond with peaks in the steepness index and maximum k_sn is 1,541 m^0.9 (Figure 9A; Table 5), whereas on Shyok River landslide dams correspond with peaks in the steepness index and maximum k_sn is 1,229 m^0.9 (Figure 9B; Table 5). However, both rivers have similar long profiles, with relatively flat sections on the plateau that have low k_sn values (Figures 9A,B), including lake areas with k_sn around 0 (Figure 9B). Many glacier dams are developed in the Hunza River, but the river steepness index shows relatively little variation (Figure 9C). Debris flow dams are associated with steeper reaches in Gilgit River, Shingo River, and Astor River (Figures 9D,G,H) and although Zanskar River has fewer dams, they also correspond with relatively steep zones (Figure 9E). In Shigar River, the steepest part of the river lies in Glacier Valley, but landslide dams have a relatively large impact on the river profile (Figure 9F).

FIGURE 9

FIGURE 9. Spatial relationships between dams and river longitudinal profile in sub-basins of the upper Indus (see Figure 8 for locations). Each graph plots dam type (debris flow, landslide and glacier) on the long profile (black line), with upstream area (orange), slope (blue) and steepness index (green), the slope and steepness index are smoothed on average for every 10 values.

TABLE 5

TABLE 5. Steepness index for each dam type (unit: m^0.9).

Figure 9 shows that there is good correspondence between dam location, the convex part of the river longitudinal profile and relatively high steepness index. Debris flow dams have the greatest influence on the river long profile, with landslide dam second and glacier dams the least (Figure 9; Table 5). The river steepness index k_sn has been widely used as an indicator of the topographic uplift in the region, with higher steepness values related to increased tectonic uplift rate (Hu et al., 2010; Wang et al., 2015). However, as we show above, steepness index is also reflective of the geomorphic response to damming, which is often neglected in tectonic studies (Korup, 2006b).

Most mass movement-generated dams on the Indus and its tributaries, some of which have impounded large lakes in the upper Indus, do not form persistent knickpoints. Factors such as size and duration of lake are also important. Also, it is likely that knickpoints may have been present initially, but the channel has now returned to an equilibrium state. There is ample evidence for ancient glacier dam blockages on the Hunza River, often associated with lake development, but our analysis shows limited geomorphic impact on the river profile, with no significant change in k_sn (Figure 9C). The Siachen Glacier in Karakoram blocked the Shyok River during the last glacial period, forming a lake that persisted from 6–27 ka based on analysis of sediment thickness; however, the river has adjusted to a new equilibrium state and there is no significant impact on the longitudinal profile (Scherler et al., 2014). Other research suggests that upstream sedimentation during damming can protect bedrock from erosion, and even after the dam outburst, the coarse gravel of the dam itself and outburst flood deposit is difficult to be eroded by normal water flow, so the knickpoints can be maintained for a long time (Ouimet et al., 2007; Korup et al., 2010a). And studies on the Yarlung Zangbo and Jinsha rivers have shown that the influence of ancient dams on river channel can last at least tens of thousands of years (Liu et al., 2015; Liu et al., 2018).

Knickpoint Formation Types

To better understand the formation mechanism, we compared the spatial distribution of knickpoints across the Indus basin with dams, known faults and lithological variations (Figure 10). We identified spatial association linking 102 knickpoints to potential causes (Figure 10; Table 6): 23 knickpoints related to debris flow dams (DKP), 27 related to landslide dams (LKP), five related to glacier dams (GKP), six related to faults (FKP), 12 related to lithological variations (LOKP), 17 in glacier valley (GVKP) and 11 knickpoints unknown (OKP). However, using spatial correlation to imply causation is problematic; knickpoints may result from a combination of multiple factors, or migrate headwards so that their location changes over time (Zhang et al., 2011). Hence, it is necessary to make a judgment based on knowledge of local topographic features, tectonic activity, timing of damming event, etc.

FIGURE 10

FIGURE 10. Distribution of knickpoint types in the upper Indus River.

TABLE 6

TABLE 6. Types of knickpoints and their height statistics (see Figure 10 for locations).

There are 55 knickpoints affected by the dams, accounting for more than half, but different types of dam have different effects on the knickpoint. We analyzed the height of the knickpoint associated with each formation type and found those associated with debris flow dams to be the greatest, with a maximum height of over 900 m (Table 6). This indicates the significant geomorphic impact of debris flow accumulation fans on the elevation profile of the channel, and may be due to the high frequency of debris flow events which makes their removal difficult. The importance of debris flows to knickpoints is supported by Fan et al. (2019b), who mapped 666 knickpoints in the Minjiang River Basin, at the eastern margin of the plateau, and found that most were related to accumulation from landslides and debris flows. Furthermore, Korup (2006b) found that no matter what the regional geological tectonic and climatic conditions are, knickpoints correspond with the location of large-scale bedrock landslide or collapse events. In the lower reaches of the Gilgit and Hunza rivers, wide valley sedimentary terraces are formed due to large landslides, with large amounts of landslide debris injected into the channel, which may affect the ability of river sediments to accrete, transport and erode (Burbank, 2002). Understanding of the geomorphic impact of dams on the river long profile, along with careful analysis of the structural and climatic background, is necessary to obtain a reasonable regional geomorphic evolution model.

Conclusion

We used TopoToolbox to extract the longitudinal profile, knickpoints and k_sn values for the Indus River basin and compared them with dam types and location obtained through remote sensing interpretation. A total of 178 dams and 102 knickpoints were identified, with 55 knickpoints related to dams. Overall, there is a good spatial correlation between dams, high steepness index k_sn and river knickpoints in the Indus basin. The impact of debris flow dams on the river longitudinal profile is more significant than that of landslide and glacial dams in the upper Indus River. Knickpoints formed by debris flow dams have a maximum height of over 900 m. The study demonstrates that dams play an important role in the evolution of river longitudinal profiles. The potential influence of dams should be considered when using river knickpoints to derive information on tectonic activity.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

LZ is mainly responsible for data extraction and analysis of the paper, WL is mainly responsible for guiding and supervising the writing, and providing ideas, XC provided project support and paper discussion, HW provide discussion and guidance on the thesis writing process, XH provides a discussion of the revision of the thesis language and the writing process, XL provide discussion and suggestions on the writing process, WS provides TopoToolbox for extracting geomorphic parameter data.

Funding

This research was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant Nos. 2019QZKK0902f), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant Nos. XDA 20030301), the National Natural Science Foundation of China (Grant Nos. 42071017 and 41771023).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

A special acknowledgement should be expressed to China-Pakistan Joint Research Center on Earth Sciences that supported the implementation of this study, thanks to Professor ZongJi Yang for his assistance in the field survey.

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