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Badlands have long been considered as model landscapes due to their perceived close relationship between form and process. The often intense features of erosion have also attracted many geomorphologists because the associated high rates of erosion appeared to offer the opportunity for studying surface processes and the resulting forms. Recently, the perceived simplicity of badlands has been questioned because the expected relationships between driving forces for erosion and the resulting sediment yield could not be observed. Further, a high variability in erosion and sediment yield has been observed across scales. Finally, denudation based on currently observed erosion rates would have lead to the destruction of most badlands a long time ago. While the perceived simplicity of badlands has sparked a disproportional (compared to the land surface they cover) amount of research, our increasing amount of information has not necessarily increased our understanding of badlands in equal terms. Overall, badlands appear to be more complex than initially assumed. In this paper, we review 40 years of research in the Zin Valley Badlands in Israel to reconcile some of the conflicting results observed there and develop a perspective on the function of badlands as model landscapes. While the data collected in the Zin Valley clearly confirm that spatial and temporal patterns of geomorphic processes and their interaction with topography and surface properties have to be understood, we still conclude that the process of realizing complexity in the "simple" badlands has a model function both for our understanding as well as perspective on all landscape systems.
Mountain rivers respond to strong earthquakes by rapidly aggrading to accommodate excess sediment delivered by co-seismic landslides. Detailed sediment budgets indicate that rivers need several years to decades to recover from seismic disturbances, depending on how recovery is defined. We examine three principal proxies of river recovery after earthquake-induced sediment pulses around Pokhara, Nepal's second largest city. Freshly exhumed cohorts of floodplain trees in growth position indicate rapid and pulsed sedimentation that formed a fan covering 150 km2 in a Lesser Himalayan basin with tens of metres of debris between the 11th and 15th centuries AD. Radiocarbon dates of buried trees are consistent with those of nearby valley deposits linked to major medieval earthquakes, such that we can estimate average rates of re-incision since. We combine high-resolution digital elevation data, geodetic field surveys, aerial photos, and dated tree trunks to reconstruct geomorphic marker surfaces. The volumes of sediment relative to these surfaces require average net sediment yields of up to 4200 t km–2 yr–1 for the 650 years since the last inferred earthquake-triggered sediment pulse. The lithological composition of channel bedload differs from that of local bedrock, confirming that rivers are still mostly evacuating medieval valley fills, locally incising at rates of up to 0.2 m yr–1. Pronounced knickpoints and epigenetic gorges at tributary junctions further illustrate the protracted fluvial response; only the distal portions of the earthquake-derived sediment wedges have been cut to near their base. Our results challenge the notion that mountain rivers recover speedily from earthquakes within years to decades. The valley fills around Pokhara show that even highly erosive Himalayan rivers may need more than several centuries to adjust to catastrophic perturbations. Our results motivate some rethinking of post-seismic hazard appraisals and infrastructural planning in active mountain regions.
Channel steepness index, k(s), is a metric derived from the stream power model that, under certain conditions, scales with relative rock uplift rate. Channel steepness index is a property of rivers, which can be relatively easily extracted from digital elevation models (DEMs). As DEM data sets are widely available for Earth and are becoming more readily available for other planetary bodies, channel steepness index represents a powerful tool for interpreting tectonic processes. However, multiple approaches to calculate channel steepness index exist. From this several important questions arise; does choice of approach change the values of channel steepness index, can values be so different that choice of approach can influence the findings of a study, and are certain approaches better than others? With the aid of a synthetic river profile and a case study from the Sierra Nevada, California, we show that values of channel steepness index vary over orders of magnitude according to the methodology used in the calculation. We explore the limitations, advantages and disadvantages of the key approaches to calculating channel steepness index, and find that choosing an appropriate approach relies on the context of a study. Given these observations, it is important that authors acknowledge the methodology used to calculate channel steepness index, to ensure that results can be contextualised and reproduced.
The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.
Himalayan water resources attract a rapidly growing number of hydroelectric power projects (HPP) to satisfy Asia's soaring energy demands. Yet HPP operating or planned in steep, glacier-fed mountain rivers face hazards of glacial lake outburst floods (GLOFs) that can damage hydropower infrastructure, alter water and sediment yields, and compromise livelihoods downstream. Detailed appraisals of such GLOF hazards are limited to case studies, however, and a more comprehensive, systematic analysis remains elusive. To this end we estimate the regional exposure of 257 Himalayan HPP to GLOFs, using a flood-wave propagation model fed by Monte Carlo-derived outburst volumes of >2300 glacial lakes. We interpret the spread of thus modeled peak discharges as a predictive uncertainty that arises mainly from outburst volumes and dam-breach rates that are difficult to assess before dams fail. With 66% of sampled HPP are on potential GLOF tracks, up to one third of these HPP could experience GLOF discharges well above local design floods, as hydropower development continues to seek higher sites closer to glacial lakes. We compute that this systematic push of HPP into headwaters effectively doubles the uncertainty about GLOF peak discharge in these locations. Peak discharges farther downstream, in contrast, are easier to predict because GLOF waves attenuate rapidly. Considering this systematic pattern of regional GLOF exposure might aid the site selection of future Himalayan HPP. Our method can augment, and help to regularly update, current hazard assessments, given that global warming is likely changing the number and size of Himalayan meltwater lakes.
Himalayan water resources attract a rapidly growing number of hydroelectric power projects (HPP) to satisfy Asia's soaring energy demands. Yet HPP operating or planned in steep, glacier-fed mountain rivers face hazards of glacial lake outburst floods (GLOFs) that can damage hydropower infrastructure, alter water and sediment yields, and compromise livelihoods downstream. Detailed appraisals of such GLOF hazards are limited to case studies, however, and a more comprehensive, systematic analysis remains elusive. To this end we estimate the regional exposure of 257 Himalayan HPP to GLOFs, using a flood-wave propagation model fed by Monte Carlo-derived outburst volumes of >2300 glacial lakes. We interpret the spread of thus modeled peak discharges as a predictive uncertainty that arises mainly from outburst volumes and dam-breach rates that are difficult to assess before dams fail. With 66% of sampled HPP are on potential GLOF tracks, up to one third of these HPP could experience GLOF discharges well above local design floods, as hydropower development continues to seek higher sites closer to glacial lakes. We compute that this systematic push of HPP into headwaters effectively doubles the uncertainty about GLOF peak discharge in these locations. Peak discharges farther downstream, in contrast, are easier to predict because GLOF waves attenuate rapidly. Considering this systematic pattern of regional GLOF exposure might aid the site selection of future Himalayan HPP. Our method can augment, and help to regularly update, current hazard assessments, given that global warming is likely changing the number and size of Himalayan meltwater lakes.
TopoToolbox is a MATLAB program for the analysis of digital elevation models (DEMs). With the release of version 2, the software adopts an object-oriented programming (OOP) approach to work with gridded DEMs and derived data such as flow directions and stream networks. The introduction of a novel technique to store flow directions as topologically ordered vectors of indices enables calculation of flow-related attributes such as flow accumulation similar to 20 times faster than conventional algorithms while at the same time reducing memory overhead to 33% of that required by the previous version. Graphical user interfaces (GUIs) enable visual exploration and interaction with DEMs and derivatives and provide access to tools targeted at fluvial and tectonic geomorphologists. With its new release, TopoToolbox has become a more memory-efficient and faster tool for basic and advanced digital terrain analysis that can be used as a framework for building hydrological and geomorphological models in MATLAB.
Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques
(2017)
The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms quantile carving and the CRS algorithm - that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.
Knickpoints in longitudinal river profiles are proxies for the climatic and tectonic history of active mountains. The analysis of river profiles commonly relies on the assumption that drainage network configurations are stable. Here, we show that this assumption must be made cautiously if changes in contributing area are fast relative to knickpoint migration rates. We studied the Parachute Creek basin in the Roan Plateau, Colorado, United States, where knickpoint retreat occurs in horizontally uniform lithology so that drainage area is the sole governing variable. In this basin, we identified an anomalous catchment in the degree to which a stream power-based model predicted knickpoint locations. The catchment is experiencing area loss as the plateau edge is eroded by cliff migration in proximity to the Colorado River. Model predictions improve if the plateau edge is assumed to have migrated over the time scale of knickpoint retreat. Finally, a Lagrangian model of knickpoint migration enabled us to study the kinematic links between drainage area loss and knickpoint migration and offered constraints on the temporal aspects of area loss. Modeled onset and amount of area loss are consistent with cliff retreat rates along the margin of the Roan Plateau inferred from the incisional history of the upper Colorado River.
Plain Language Summary The 2015 Gorkha earthquake in Nepal caused severe losses in the hydropower sector. The country temporarily lost similar to 20% of its hydropower capacity, and >30 hydropower projects were damaged. The projects hit hardest were those that were affected by earthquake-triggered landslides. We show that these projects are located along very steep rivers with towering sidewalls that are prone to become unstable during strong seismic ground shaking. A statistical classification based on a topographic metric that expresses river steepness and earthquake ground acceleration is able to approximately predict hydropower damage during future earthquakes, based on successful testing of past cases. Thus, our model enables us to estimate earthquake damages to hydropower projects in other parts of the Himalayas. We find that >10% of the Himalayan drainage network may be unsuitable for hydropower infrastructure given high probabilities of high earthquake damages.
The assessment of uncertainty is a major challenge in geomorphometry. Methods to quantify uncertainty in digital elevation models (DEM) are needed to assess and report derivatives such as drainage basins. While Monte-Carlo (MC) techniques have been developed and employed to assess the variability of second-order derivatives of DEMs, their application requires explicit error modeling and numerous simulations to reliably calculate error bounds. Here, we develop an analytical model to quantify and visualize uncertainty in drainage basin delineation in DEMs. The model is based on the assumption that multiple flow directions (MFD) represent a discrete probability distribution of non-diverging flow networks. The Shannon Index quantifies the uncertainty of each cell to drain into a specific drainage basin outlet. In addition, error bounds for drainage areas can be derived. An application of the model shows that it identifies areas in a DEM where drainage basin delineation is highly uncertain owing to flow dispersion on convex landforms such as alluvial fans. The model allows for a quantitative assessment of the magnitudes of expected drainage area variability and delivers constraints for observed volatile hydrological behavior in a palaeoenvironmental record of lake level change. Since the model cannot account for all uncertainties in drainage basin delineation we conclude that a joint application with MC techniques is promising for an efficient and comprehensive error assessment in the future.
Digital flow networks derived from digital elevation models (DEMs) sensitively react to errors due to measurement, data processing and data representation. Since high-resolution DEMs are increasingly used in geomorphological and hydrological research, automated and semi-automated procedures to reduce the impact of such errors on flow networks are required. One such technique is stream-carving, a hydrological conditioning technique to ensure drainage connectivity in DEMs towards the DEM edges. Here we test and modify a state-of-the-art carving algorithm for flow network derivation in a low-relief, agricultural landscape characterized by a large number of spurious, topographic depressions. Our results show that the investigated algorithm reconstructs a benchmark network insufficiently in terms of carving energy, distance and a topological network measure. The modification to the algorithm that performed best, combines the least-cost auxiliary topography (LCAT) carving with a constrained breaching algorithm that explicitly takes automatically identified channel locations into account. We applied our methods to a low relief landscape, but the results can be transferred to flow network derivation of DEMs in moderate to mountainous relief in situations where the valley bottom is broad and flat and precise derivations of the flow networks are needed.
Geomorphic footprints of past large Himalayan earthquakes are elusive, although they are urgently needed for gauging and predicting recovery times of seismically perturbed mountain landscapes. We present evidence of catastrophic valley infill following at least three medieval earthquakes in the Nepal Himalaya. Radiocarbon dates from peat beds, plant macrofossils, and humic silts in fine-grained tributary sediments near Pokhara, Nepal’s second-largest city, match the timing of nearby M > 8 earthquakes in ~1100, 1255, and 1344 C.E. The upstream dip of tributary valley fills and x-ray fluorescence spectrometry of their provenance rule out local sources. Instead, geomorphic and sedimentary evidence is consistent with catastrophic fluvial aggradation and debris flows that had plugged several tributaries with tens of meters of calcareous sediment from a Higher Himalayan source >60 kilometers away.
Drainage divide networks
(2020)
Drainage divides are organized into tree-like networks that may record information about drainage divide mobility. However, views diverge about how to best assess divide mobility. Here, we apply a new approach of automatically extracting and ordering drainage divide networks from digital elevation models to results from landscape evolution model experiments. We compared landscapes perturbed by strike-slip faulting and spatiotemporal variations in erodibility to a reference model to assess which topographic metrics (hillslope relief, flow distance, and chi) are diagnostic of divide mobility. Results show that divide segments that are a minimum distance of similar to 5 km from river confluences strive to attain constant values of hillslope relief and flow distance to the nearest stream. Disruptions of such patterns can be related to mobile divides that are lower than stable divides, closer to streams, and often asymmetric in shape. In general, we observe that drainage divides high up in the network, i.e., at great distances from river confluences, are more susceptible to disruptions than divides closer to these confluences and are thus more likely to record disturbance for a longer time period. We found that across-divide differences in hillslope relief proved more useful for assessing divide migration than other tested metrics. However, even stable drainage divide networks exhibit across-divide differences in any of the studied topographic metrics. Finally, we propose a new metric to quantify the connectivity of divide junctions.
We propose a novel way to measure and analyze networks of drainage divides from digital elevation models. We developed an algorithm that extracts drainage divides based on the drainage basin boundaries defined by a stream network. In contrast to streams, there is no straightforward approach to order and classify divides, although it is intuitive that some divides are more important than others. A meaningful way of ordering divides is the average distance one would have to travel down on either side of a divide to reach a common stream location. However, because measuring these distances is computationally expensive and prone to edge effects, we instead sort divide segments based on their tree-like network structure, starting from endpoints at river confluences. The sorted nature of the network allows for assigning distances to points along the divides, which can be shown to scale with the average distance downslope to the common stream location. Furthermore, because divide segments tend to have characteristic lengths, an ordering scheme in which divide orders increase by 1 at junctions mimics these distances. We applied our new algorithm to the Big Tujunga catchment in the San Gabriel Mountains of southern California and studied the morphology of the drainage divide network. Our results show that topographic metrics, like the downstream flow distance to a stream and hillslope relief, attain characteristic values that depend on the drainage area threshold used to derive the stream network. Portions along the divide network that have lower than average relief or are closer than average to streams are often distinctly asymmetric in shape, suggesting that these divides are unstable. Our new and automated approach thus helps to objectively extract and analyze divide networks from digital elevation models.
Graph theory has long been used in quantitative geography and landscape ecology and has been applied in Earth and atmospheric sciences for several decades. Recently, however, there have been increased, and more sophisticated, applications of graph theory concepts and methods in geosciences, principally in three areas: spatially explicit modeling, small-world networks, and structural models of Earth surface systems. This paper reviews the contrasting goals and methods inherent in these approaches, but focuses on the common elements, to develop a synthetic view of graph theory in the geosciences. Techniques applied in geosciences are mainly of three types: connectivity measures of entire networks; metrics of various aspects of the importance or influence of particular nodes, links, or regions of the network; and indicators of system dynamics based on graph adjacency matrices. Geoscience applications of graph theory can be grouped in five general categories: (1) Quantification of complex network properties such as connectivity, centrality, and clustering; (2) Tests for evidence of particular types of structures that have implications for system behavior, such as small-world or scale-free networks; (3) Testing dynamical system properties, e.g., complexity, coherence, stability, synchronization, and vulnerability; (4) Identification of dynamics from historical records or time series; and (5) spatial analysis. Recent and future expansion of graph theory in geosciences is related to general growth of network-based approaches. However, several factors make graph theory especially well suited to the geosciences: Inherent complexity, exploration of very large data sets, focus on spatial fluxes and interactions, and increasing attention to state transitions are all amenable to analysis using graph theory approaches. (C) 2015 Elsevier B.V. All rights reserved.
In this study, we investigate how immersive 3D geovisualization can be used in higher education. Based on MacEachren and Kraak's geovisualization cube, we examine the usage of immersive 3D geovisualization and its usefulness in a research-based learning module on flood risk, called GEOSimulator. Results of a survey among participating students reveal benefits, such as better orientation in the study area, higher interactivity with the data, improved discourse among students and enhanced motivation through immersive 3D geovisualization. This suggests that immersive 3D visualization can effectively be used in higher education and that 3D CAVE settings enhance interactive learning between students.
The Norwegian traffic network is impacted by about 2000 landslides, avalanches, and debris flows each year that incur high economic losses. Despite the urgent need to mitigate future losses, efforts to locate potential debris flow source areas have been rare at the regional scale. We tackle this research gap by exploring a minimal set of possible topographic predictors of debris flow initiation that we input to a Weights-of-Evidence (WofE) model for mapping the regional susceptibility to debris flows in western Norway. We use an inventory of 429 debris flows that were recorded between 1979 and 2008, and use the terrain variables of slope, total curvature, and contributing area (flow accumulation) to compute the posterior probabilities of local debris flow occurrence. The novelty of our approach is that we quantify the uncertainties in the WofE approach arising from different predictor classification schemes and data input, while estimating model accuracy and predictive performance from independent test data. Our results show that a percentile-based classification scheme excels over a manual classification of the predictor variables because differing abundances in manually defined bins reduce the reliability of the conditional independence tests, a key, and often neglected, prerequisite for the WofE method. The conditional dependence between total curvature and flow accumulation precludes their joint use in the model. Slope gradient has the highest true positive rate (88%), although the fraction of area classified as susceptible is very large (37%). The predictive performance, i.e. the reduction of false positives, is improved when combined with either total curvature or flow accumulation. Bootstrapping shows that the combination of slope and flow accumulation provides more reliable predictions than the combination of slope and total curvature, and helps refining the use of slope-area plots for identifying morphometric fingerprints of debris flow source areas, an approach used outside the field of landslide susceptibility assessments.