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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.
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.
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.
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.