Abstract. Ice cliffs within a supraglacial debris cover have been identified as a source for high ablation relative to the surrounding debris-covered area. Due to their small relative size and steep orientation, ice cliffs are difficult to detect using nadir-looking space borne sensors. The method pre- sented here uses surface slopes calculated from digital el- evation model (DEM) data to map ice cliff geometry and produce an ice cliff probability map. Surface slope thresh- olds, which can be sensitive to geographic location and/or data quality, are selected automatically. The method also at- tempts to include area at the (often narrowing) ends of ice cliffs which could otherwise be neglected due to signal sat- uration in surface slope data. The method was calibrated in the eastern Alaska Range, Alaska, USA, against a control ice cliff dataset derived from high-resolution visible and thermal data. Using the same input parameter set that performed best in Alaska, the method was tested against ice cliffs manually mapped in the Khumbu Himal, Nepal. Our results suggest the method can accommodate different glaciological settings and different DEM data sources without a data intensive (high- resolution, multi-data source) recalibration.
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On 12 ice cliffs ablation was measured perpendicular to the surface. Mean degree day factors for north facing (0.0043 m d −1 ◦ C −1 ), south facing (0.0054 m d −1 ◦ C −1 ) and east/west facing cliffs (0.0052 m d −1 ◦ C −1 ) were calculated and applied within the model. For supraglacial lakes the melt rates at the lake bottom are estimated using the same de- gree day factor – debris cover thickness regression, but in- stead of calculating the sum of positive degree days based on the air temperature record, the overlying water is assumed to have a constant temperature of 4 ◦ C for the period from 1 May 2010 to 31 October 2010. This assumption is sup- ported by the work of Xin et al. (2012), who monitored the thermal regime of a supraglacial lake during ablation sea- son at Koxkar Glacier in 2008. One drawback of the present model is that the lateral melting below the water surface in the ponds is not included, because the dynamic evolution of the debris mantle is not incorporated. Taking into consider- ation that lakes occupy only 0.36 % of the glacier area, the error arising from this is rather small for one ablation season. The mapping and area calculations of supraglacial lakes and steep ice cliffs were carried out with the stereo image data provided by the Ikonos product (Fig. 3). For this pur-
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However, less attention has been paid to fine debris and dust deposition at the glacier melting surface. A first attempt to parameterize not only snow albedo variability but also the ice albedo on a debris-free glacier was performed by Brock et al. (2000). In spite of the good results they obtained ana- lyzing snow-covered areas, their evaluation of the impact of debris cover on ice albedo was less accurate. In fact, they assessed the debris cover using only a 0.5 m 2 quadrat and basing their investigation on just two criteria (i.e., cumulative melt and number of days, both calculated following exposure of the ice surface). Recently, Pope and Rees (2014) inves- tigated the spectral responses of different ash/debris cover types on the glaciers of Midtre Lovénbreen (Svalbard) and Langjökull (Iceland). These studies suggested the need for further research to standardize the measurements of fine de- bris and dust at the glacier ice surface, thus avoiding the use of surrogates unable to fully describe debris coverage and its seasonal variability.
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Abstract. The Karakoram range of the Hindu-Kush Hi- malaya is characterized by both extensive glaciation and a widespread prevalence of surficial debris cover on the glaciers. Surface debris exerts a strong control on glacier surface-energy and mass fluxes and, by modifying surface boundary conditions, has the potential to alter atmosphere– glacier feedbacks. To date, the influence of debris on Karako- ram glaciers has only been directly assessed by a small num- ber of glaciological measurements over short periods. Here, we include supraglacial debris in a high-resolution, interac- tively coupled atmosphere–glacier modeling system. To in- vestigate glaciological and meteorological changes that arise due to the presence of debris, we perform two simulations using the coupled model from 1 May to 1 October 2004: one that treats all glacier surfaces as debris-free and one that in- troduces a simplified specification for the debris thickness. The basin-averaged impact of debris is a reduction in ab- lation of ∼ 14 %, although the difference exceeds 5 m w.e. on the lowest-altitude glacier tongues. The relatively mod- est reduction in basin-mean mass loss results in part from non-negligible sub-debris melt rates under thicker covers and from compensating increases in melt under thinner de- bris, and may help to explain the lack of distinct differ- ences in recent elevation changes between clean and debris- covered ice. The presence of debris also strongly alters the surface boundary condition and thus heat exchanges with the atmosphere; near-surface meteorological fields at lower elevations and their vertical gradients; and the atmospheric boundary layer development. These findings are relevant for
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5.2 Chronological evolution and process interactions On the basis of their dynamic behaviour we distinguish four distinct phases over the entire observation period since 1879. In the first phase, between 1879 and 1977, as a response to the atmospheric warming in the mid-19th century, the mass balance of Zmuttgletscher turned negative (Fig. 3), first only slightly (1879–1946: − 0.09 ± 0.12 m. w.e. yr −1 ), then more strongly (1946–1977: − 0.67 ± 0.16 m. w.e. yr −1 ), lead- ing to glacier thinning and retreat. At the same time, the debris-covered area increased from 2.75 ± 0.2 km 2 in 1859 to 3.67 ± 0.01 km 2 in 1961. Rising air temperatures are prob- ably the main reason for the increasing debris extent as they lead to a rise of the rain–snow transition altitude as well as higher ice ablation. Therefore, debris emergence accelerates and moves further up-glacier (Stokes et al., 2007). Decreas- ing ice flow further supports the development of a continuous debris cover of substantial thickness as the emerging debris is evacuated more slowly, and the debris has more time to melt out and thicken (Kirkbride and Deline, 2013). In the mid- 20th century, a continuous debris cover existed on the lower part of the tongue, likely already responsible for a flattening of the mass balance gradient. Ice cliffs and supraglacial melt- water channels already existed in the lowest, debris-covered part of Zmuttgletscher in 1930 (Fig. S17) and thereafter in- creased in area until 1977. These surface features are partly responsible for increased thinning in these areas close to the terminus compared to further up-glacier.
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Let us consider two idealised model glaciers. Glacier A does not have any debris cover and has a linear mass-balance profile. Glacier B has a supraglacial debris cover on its lower ablation zone where the ablation rate saturates to a value of −2 m yr −1 (Fig. 1b). This idealised mass-balance profile for the debris-covered glacier is motivated by data from a Hi- malayan glacier (Banerjee and Azam, 2016). Similar sim- plified mass-balance profiles have been used to analyse the response of the debris-covered Himalayan glaciers (Baner- jee and Shankar, 2013; Banerjee and Azam, 2016). In a real glacier, the possible variability of debris thickness and ephemeral thermokarst features (ponds and ice cliffs) cause significant spatial variation of the melt rate in the debris- covered parts of the glacier. However, a relatively fast ad- vection of these surface features would imply that a long- term mean melt rate at a specific location is a well-defined quantity. This justifies the simplified mass-balance profile employed here. Further, the observed thinning rate values in the Himalayas are obtained for a large set of glaciers so that the possible effects of specific details of mass-balance profile of individual glaciers would be averaged out.
of continuous debris cover travelled quickly to the proglacial stream, with mean u of the upper glacier traces (S10–S15) being 0.56 m s −1 . These traces also generally had single peaked return curves (Figs. 6a and 7d–f) and relatively high percentage dye re- turns (P r ), confirming that the majority of the water was routed e ffi ciently. Most streams from the lower glacier had low u (the average for all lower glacier injection points was
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5.1 In situ spectra and debris geochemical composition Several snow and ice spectral signatures (clean snow, fine particulate covered snow, granitic gravel on snow, bare ice, ice with schistic pebbles, and full schistic debris cover) col- lected in the upper Khumbu glacier are presented in Fig. 3. As glacier surface dust and debris cover increases, VNIR re- flectance decreases, as visualized by the “granitic gravel on snow” compared with “clean snow” and “schistic pebbles on ice” compared with “bare ice” spectral signatures in Fig. 3. Both fine particulates and granitic gravel reduce VNIR re- flectance of clean snow, with fine particulates displaying an absorption feature minima at approximately 0.5 µm. Larger scale gravel shows a more marked broad reduction in snow reflectance in VNIR, with absorption features beginning ear- lier, at approximately 0.38 µm. Full supraglacial debris cover results in loss of characteristic snow and ice VNIR reflectance absorption features, and debris mineralogy domi- nates the VNIR-SWIR reflectance signature. SWIR in partic- ular is used to differentiate mineral components, while VNIR can signal transition metal abundance. To note, minor de- tector related signal influences can be seen in some Fig. 3 spectra at 1.0 and 1.8 µm (further discussed in Painter, 2011). Satellite reflectance from the ASTER AST 07XT data prod- uct is plotted for corresponding bare ice and full schistic de- bris, and exemplifies the glacier surface composition differ- entiation capabilities of satellite derived surface reflectance.
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Abstract. The glacier coverage in the Caucasus Mountains underwent considerable changes during the last decades. In some regions, the observed reduction in glacier area is com- parable to those in the European Alps and the extent of supra-glacial debris increased on many glaciers. Only a few glaciers in the Caucasus are monitored on a regular basis, while for most areas no continuous field measurements are available. In this study, regional differences of the conditions for glacier melt with a special focus on debris covered glacier tongues in the well-studied Adyl-su basin on the northern slope of the Caucasus Mountains (Russia) is compared with the Zopkhito basin which has similar characteristics but is located on the southern slope in Georgia. The paper focuses on the effect of supra-glacial debris cover on glacier summer melt. There are systematic differences in the distribution and increase of the debris cover on the glaciers of the two basins. In the Adyl-su basin an extensive debris cover on the glacier tongues is common, however, only those glacier tongues that are positioned at the lowest elevations in the Zopkhito basin show a considerable extent of supra-glacial debris. The ob- served increase in debris cover is considerably stronger in the north. Field experiments show that thermal resistance of the debris cover in both basins is somewhat higher than in other glaciated regions of the world, but there is also a significant difference between the two regions. A simple ablation model accounting for the effect of debris cover on ice melt shows that melt rates are considerably higher in the northern basin despite a wider debris distribution. This difference between
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TIR imaging and especially thermal inertia mapping of ice-debris landforms is still a relatively novel approach that may provide insights into surface energy fluxes of these cryospheric features with variable debris cover thicknesses. ASTER-derived daytime LST on debris-covered glacier has been found to be several degrees lower than in adjacent ar- eas not underlain by massive ice (Taschner and Ranzi, 2002), and it may serve as a proxy for debris cover thickness, at least for thin debris covers <0.6 m (Mihalcea et al., 2008). It fur- ther has been suggested that ATI differs between the surfaces of ice-debris landforms and surrounding areas (Piatek et al., 2007; Piatek, 2009). A quantitative assessment of this con- jecture is presented in this study. Bare, dry soil and cloudless skies are required to apply this approach (Watson, 1975), and these conditions are perfectly met in the summer-dry Andes during the snow-free period.
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In order to show the generality of inferences made by Scherler et al. (2011b), we also change the bed slope in our hypothetical model. Changing the linear bed slope leads to similar relationships between debris cover percentage, AAR, and surface velocity. Notable differences occur primarily when the bed slope is reduced (Fig. 11c and d). With a re- duced bed slope the initial debris-free steady state glacier is 3 times longer than the steady state debris free glacier. Even with the same hillslope debris fluxes as the simulations in Fig. 11a and b, the reduced bed slope leads to reduced asym- metry in the steady state debris-covered glacier surface ve- locities (Fig. 11d). With a linear mass balance profile and linear bed slope, changing the bed slope will have a similar effect to changing the mass balance gradient. The specific relationship of glacier response to debris is therefore also dependent on glacier size, bed slope, and the environmen- tal mass balance gradient. Ultimately, our exploration shows that, independent of parameter selection (e.g., not dependent on bed slope or mass balance profile selection), our model reproduces basic patterns inferred from real debris-covered glaciers, which lends support to our model framework, while also providing quantitative, theoretical support to previous data-based observations.
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Initially, it should be noted, that truncated half cone-shaped elements of construction are located by 3 rows. The distance between the row is L=10 m, because L is small, in the calculation is not taking into acount loss of debris flow energy on the lengh during throw the debris flow from I row of truncated half cone-shaped elements to III row. The calculating formula of hitting force of debris flow on the construction is follow:
Planet formation requires the hierarchical growth of dust grains to pebbles and thereafter to larger bodies eventually ending up at asteroids and comets — the planetesimals from which exoplanets form ( Perryman 2011, p. 426; Armi- tage 2013 ) . At the same time, collisions between these planetesimals produce the dust grains we observe as the visible components of debris disks. Since planetesimals are key to production of both planets and dusty debris, one might expect the properties of planets and debris around a star to be mutually dependent. This expectation has been strengthened by the direct imaging of several exoplanet systems around debris disk host stars and indirectly by the structural features observed in many debris disks ( warps, off-sets, asymmetries ) , which are often inferred to be due to the gravitational perturbation of the debris by one or more unseen exoplanet ( s ) ( see reviews by Wyatt 2008; Krivov 2010; Moro-Martin 2013 ) .
We examine the final output of the integrated simulation first. Erosion plays an important role in the volume magnifica- tion of debris flows. The final erosion depths in the eroded areas are shown in Fig. 10a. The most eroded areas during the Xiaojiagou debris flow event were in channels, where a huge amount of loose solid material was present (Chen et al., 2012). Loose deposits on the hillslopes also eroded after the landslide bodies detached from their original locations and slid down the slopes. The distribution of the eroded areas reflects that the debris flows were initiated from both slope failures and surface erosion, then developed along the chan- nels by further erosion and entrainment of the slope failure materials; these are the two mechanisms considered in the integrated model. The distribution of the maximum flow ve- locity is shown in Fig. 10b, with the maximum value being 9.5 m s −1 . This value is very close to that from EDDA 1.0 (9.1 m s −1 ). The slightly larger value of flow velocity from EDDA 2.0 is attributed to the consideration of the extra sur- face runoff within domain two created when using EDDA 1.0 (Fig. 1). The maximum velocity occurs in the ravine chan- nels, indicating that the debris flow moves very rapidly.
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Optimizationof EDM employing rotary tool assisted with a rotating magnetic field was studied by R. Teimouri and H. Baseri. The Fig.3 shows the experimental setup consisting of an electro motor and belt mechanism mounted on a machine. The rotational speed of the machine is being controlled by LS600 inverter. Two magnetic poles with various intensities were attached onto the inner surface of a cylinder with a central through hole which establishes the magnetic field around the work and tool electrode. It was found that high magnetic field intensity caused expansion of plasma channel that affects the MRR, but provides a better surface finish. Lower field intensities were found to provide better ionization by resisting the expansion of the plasma channel providing better MRR and on the other hand affecting the surface roughness. At higher rotational speeds there was an improvement in MRR as the accumulation of debris in the machining gap was lowered which in turn lowered the surface roughness. Hence, a better surface finish can be achieved.
Debris cover in the ablation area of most of the Himalayan glaciers acts as an insulator and renders the ablation zone less sensitive to melting . This protects the termini at lower altitudes (below 4000 m) where maintenance of the glacier mass elsewhere in the world is not possible due to unfavourable temperatures [9,10]. Co and cross polarized SAR data are employed to derived debris size. The average size of debris over Chhota Shigri glacier is 50 – 100 mm. Over the Gangotri glacier it is 100 – 200 mm, however, the Zemu glacier has 300 – 400 mm average debris size. The accuracy of the classification is ± 50 mm. The large size debris over the Zemu glacier protects the glacier ice to some extent during the on-set of melting. However, according to , thick debris cover accelerates melting during rainfall events by transferring heat from debris to ice through liquid
the right side in the above equation is generally called Froude–Krylov force, which relates to the pressure gradient of the flow field. The second term on the right side in the above equation is hydrodynamic mass force, which is in proportion to relative acceleration between fluid and tsunami debris. The third term on the right side in the above equation is drag force, which is proportional to the square of relative velocity between fluid and tsunami debris. In this simulation, a cylindrical shape is used as the shape of impacted structure. And the fluid velocity u in front of the impacted structure was expressed by following equation 1) .
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We use Eq. 2 to evaluate the susceptibility to debris-flows for both classes, in comparison with the number of hollows in the entire study area. Geology and slope were reclassified using values ranging between 1 and 5. The value 5 was given to the classes that were considered potentially most unstable. For type-r deposits, the Fucoidi Formation exhibits the high- est weight in debris-flow evaluation. For type-n deposits, the Fucoidi and Massiccio Formations are relevant. Two slope classes are important, namely 0 ◦ –13 ◦ and 40 ◦ –53 ◦ , for both type-n and type-n deposits (Fig. 7). For the parameters de- scribing the distance from the streams and the distance from the faults, we estimated the weight using a heuristic proce- dure, which consisted in overlaying the maps representing these two parameters on the landslide hollow maps in a GIS environment. By map intersection we evaluated the relation- ship between the presence of the factor and that of the hol- lows. Inspection of the results revealed a relation between the presence of these factors and that of the hollows. To dis- criminate the areas most susceptible, a buffer of 50 m was considered around both the faults and the drainage. Weights were assigned decreasing linearly within the mapped area of influence (Table 5).
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6. The effect, not negligible, like Sosio et al. (2006) says, of the frictional component owing to the coarse fraction that is not possible to include in the laboratory analy- sis. This means that there is an underestimation of the yield strength coefficients derived by direct measure- ments due to the maximum grain size used in the rhe- ological analyses (Coussot et al., 1998; Iverson, 2003). In the present case, the material with the grain size di- ameter up to 0.063 mm is representing 20.4% of the whole sieved material. This results in a sample that is not representative of the total grain-size distribution (Ancey, 2007; Iverson, 2003). For the whole grain size distribution, the yield strength value results both from colloidal interactions, provided by the finer frac- tions, and from the frictional contacts, experienced by the coarse-grained material (Rodine et al., 1976; Cous- sot et al., 1998; Iverson, 1997, 2003). In the Fella sx event, with the amount of the coarse debris involved (up to 20% coarser than 0.2 m), it is reasonable to suppose that the grain to grain interactions are not negligible.
The remote survey method may over-estimate the amount of debris noticed from the footpath by the average visitor, as it is likely that a member of the public would not observe the survey area in as much detail as the remote surveyor (Dallimer et al., 2012). However, the survey effort expended when sampling odo- nates and debris items was comparable, so this method can be applied to indicate the relative noticeability of these components. Alternative methods such as visitor surveys could have been used to more realistically quantify the numbers of odonates and debris that were observed from the footpath. However, such methods would not have allowed the actual number of odonates and debris present to be measured, because it would not have been possible to simultaneously survey the entire area within the visitor ′ s line of sight. It would therefore not have been possible to quantify the observation rate of these components. At some case study sites it may be possible to carry out an additional ﬁ eld survey of visitors to validate our method, but this was not feasible at Fishlake due to the low numbers of visitors to the site. Informal discussions with visitors suggested that qualitatively the patterns observed in the choice experiment were realistic; visitors perceived odonates and other wildlife positively, and the quantity of debris at the site was a common complaint.