High spatial resolution data acquisition for the geosciences: kite aerial photography

This paper highlights the requirement for very high resolution (<0·25 m) elevation data for quantitative and qualitative morphometric analyses. Traditional techniques for high resolution data capture (e.g. airborne, heliborne) are prohibitively expensive for small studies and therefore a kite‐based platform was developed, in conjunction with a consumer non‐metric digital camera, for data capture. The combination of kite and digital camera is more generally termed kite aerial photography (KAP). The accuracy of data derived by digital photogrammetry and imagery acquired using a kite based non‐metric camera is assessed by three experiments: one on smooth terrain, one on tor terrain and one on a glaciofluvial esker. Ground control targets were surveyed at all three sites, with the imagery subsequently processed using the Leica Photogrammetry Suite. The results demonstrate that the method can extract a high number of sampling points at high accuracy, provided that there is suitable image texture across the site. However, final judgment concerning the suitability of derived data is dependent upon an understanding of measurement variability and user quantification of acceptable accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

Pixel‐scale evaluation of SSM/I sea‐ice algorithms in the marginal ice zone during early fall freeze‐up

Observed reduction in recent sea ice areal extent and thickness has focused attention on the fact that the Arctic marine system appears to be responding to global‐scale climate variability and change. Passive microwave remote‐sensing data are the primary source underpinning these reports, yet problems remain in geophysical inversion of information on ice type and concentration. Uncertainty in sea‐ice concentration (SIC) retrievals is highest in the summer and fall, when water occurs in liquid phase within the snow–sea‐ice system. Of particular scientific interest is the timing and rate of new ice formation due to the control that this form of sea ice has on mass, energy and gas fluxes across the ocean–sea‐ice–atmosphere interface. In this paper we examine the critical fall freeze‐up period using in situ data from a ship‐based and aerial survey programme known as the Canadian Arctic Shelf Exchange study combined with microwave and optical Earth observations data. Results show that: (1) the overall physical conditions observed from aerial survey photography were well matched with coincident moderate‐resolution imaging spectroradiometer data and Radarsat ScanSAR imagery; (2) the shortwave albedo was linearly related to old ice concentration derived from survey photography; (3) the three SSM/I SIC algorithms (NASA Team (NT), NASA Team 2 (NT2), and Bootstrap (BT)) showed considerable discrepancies in pixel‐scale comparison with the Radarsat ScanSAR SICs well calibrated by the aerial survey data. The major causes of the discrepancies are attributed to (1) the inherent inability to detect the new thin ice in the NT and BT algorithms, (2) mismatches of the thin‐ice tie point of the NT2 algorithm, and (3) sub‐pixel ambiguity between the thin ice and the mixture of open water and sea ice. These results suggest the need for finer resolution of passive microwave sensors, such as AMSR‐E, to improve the precision of the SSM/I SIC algorithms in the marginal ice zone during early fall freeze‐up. Copyright © 2006 John Wiley & Sons, Ltd.     

Near real time satellite imagery to support and verify timely flood modelling

The study investigates the capability of coarse resolution synthetic aperture radar (SAR) imagery to support flood inundation models. A hydraulic model of a 98‐km reach of the River Po (Northern Italy) was calibrated on the October 2000 high‐magnitude flood event with extensive and high‐quality field data. During the June 2008, low‐magnitude flood event a SAR image was acquired and processed in near real time (NRT) in order to provide adequate data for quick verification and recalibration of the hydraulic model. Copyright © 2009 John Wiley & Sons, Ltd.     

Semi‐automated GIS techniques for detecting floodplain earthworks

Levees, channels and water storages built on the world's floodplain wetlands control flows for irrigation, flood mitigation and erosion management. Assessing their distribution and hydrological impacts through time and across broad extents is limited by significant costs and technical challenges. We tested the effectiveness of three new semi‐automated geographic information systems and traditional visual interpretation techniques for detecting earthworks. We used commercially or freely available two‐dimensional and three‐dimensional spatial imagery within 19 quadrats in an agricultural floodplain of the Murray–Darling Basin, southeastern Australia. Semi‐automated digital elevation model (DEM) analysis performed best for spatial accuracy (78% of earthworks correctly predicted within 25 m), overall classification accuracy (97.7%) and kappa (0.64), compared with traditional visual interpretation techniques using Landsat TM (52%, 96.3%, 0.39), SPOT (53%, 95.8%, 0.27) and aerial photography (72%, 97.2%, 0.31). DEM analysis also outperformed semi‐automated image segmentation (16%, 93%, 0.29) and integrated analysis (75%, 96.0%, 0.43) that used spectral information. Semi‐automated techniques were slow (DEM analysis: 27 418 s/km²; integrated analysis: 27 737 s/km²; and image segmentation: 1439 s/km²) compared with visual interpretation (Landsat TM: 109 s/km²; SPOT: 166 s/km²; and aerial photography: 276 s/km²); however, processing speed of semi‐automated techniques can be further increased without compromising accuracy. Semi‐automated techniques also offered operational autonomy following model calibration. High quality, cost‐effective earthwork mapping techniques, particularly the semi‐automated techniques in this study, are critical for understanding and managing ecosystem health, flood risk and water security in developed floodplains worldwide and should be implemented by governing institutions. Copyright © 2012 John Wiley & Sons, Ltd.     

An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data

Reliable and prompt information on river ice condition and extent is needed to make accurate hydrological forecasts to predict ice jams breakups and issue timely flood warnings. This study presents a technique to detect and monitor river ice using observations from the MODIS instrument onboard the Terra satellite. The technique incorporates a threshold‐based decision tree image classification algorithm to process MODIS data and to determine the extent of ice. To differentiate between ice‐covered and ice‐free pixels within the riverbed, the algorithm combines observations in the visible and near‐infrared spectral bands. The developed technique presents the core of the MODIS‐based river ice mapping system, which has been developed to support National Oceanic and Atmospheric Administration NWS's operations. The system has been tested over the Susquehanna River in northeastern USA, where ice jam events leading to spring floods are a frequent occurrence. The automated algorithm generates three products: daily ice maps, weekly composite ice maps and running cloud‐free composite ice maps. The performance of the system was evaluated over nine winter seasons. The analysis of the derived products has revealed their good agreement with the aerial photography and with in situ observations‐based ice charts. The probability of ice detection determined from the comparison of the product with the high‐resolution Landsat imagery was equal to 91%. A consistent inverse relationship was found between the river discharge and the ice extent. The correlation between the discharge and the ice extent as determined from the weekly composite product reached 0.75. The developed CREST River Ice Observation System has been implemented at National Oceanic and Atmospheric Administration–Cooperative Remote Sensing Science and Technology Center as an operational Web tool allowing end users and forecasters to assess ice conditions on the river. Copyright © 2012 John Wiley & Sons, Ltd.     

Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps

In recent years, fluvial remote sensing has seen considerable progress in terms of methods capable of system scale characterisation of river catchments. One key development is automated grain size mapping. It has been shown that high resolution aerial photography can be used to automatically produce grain size maps over entire rivers. However, current aerial grain size mapping procedures all require field calibration data. The collection of such data can be costly and problematic in the case of remote areas. This paper presents a method developed to remove the need for field based calibration data. Called ‘aerial photosieving’, this method consists of using the same very high resolution aerial imagery intended for grain size map production to visually measure particle sizes on‐screen in order to provide calibration data. The paper presents a rigorous comparison of field‐based photosieving calibration data and aerial photosieving calibration data. Statistical tests are used to demonstrate that aerial photosieving gives similar results when compared with field‐based data with only a slight systematic overprediction. The new aerial photosieving method therefore simplifies the overall procedure required for the production of grain size maps and thus improves the cost‐effectiveness and potential availability of this new fluvial remote sensing technology. Copyright © 2010 John Wiley &Sons, Ltd.     

Quantifying different riverbank erosion processes during an extreme flood event

Riverbank erosion is a major contributor to catchment sediment budgets. At large spatial scales data is often restricted to planform channel change, with little information on process distributions and their sediment contribution. This study demonstrates how multi‐temporal LiDAR and high resolution aerial imagery can be used to determine processes and volumes of riverbank erosion at a catchment scale. Remotely sensed data captured before and after an extreme flood event, enabled a digital elevation model of difference (DoD) to be constructed for the channel and floodplain. This meant that: the spatial area that could be assessed was extensive; three‐dimensional forms of bank failures could be mapped at a resolution that enabled process inference; and the volume and rates of different bank erosion processes over time could be assessed. A classification of riverbank mass failures, integrating form and process, identified a total of 437 mass failure polygons throughout the study area. These were interpreted as wet flow mass failures based on the presence of a well defined scarp wall and the absence of failed blocks on the failure floor. The failures appeared to be the result of: bank exfiltration, antecedent moisture conditions preceding the event, and the historic development of the channel. Using one‐dimensional hydraulic modelling to delineate geomorphic features within the main boundary of the macrochannel, an estimated 1 466 322 m² of erosion was interpreted as fluvial entrainment, occurring across catchment areas from 30 to 1668 km². Only 8% of the whole riverbank planform area was occupied by mass failures, whilst fluvial entrainment covered 33%. A third of the volume of material eroded came from mass failures, even though they occupied 19% of the eroded bank area. The availability of repeat LiDAR surveys, combined with high‐resolution aerial photography, was very effective in erosion process determination and quantification at a large spatial scale. Copyright © 2013 John Wiley & Sons, Ltd.     

The use of remotely sensed land cover to derive floodplain friction coefficients for flood inundation modelling

Remotely sensed land cover was used to generate spatially‐distributed friction coefficients for use in a two‐dimensional model of flood inundation. Such models are at the forefront of research into the prediction of river flooding. Standard practice, however, is to use single (static) friction coefficients on both the channel and floodplain, which are varied in a calibration procedure to provide a “best fit” to a known inundation extent. Spatially‐distributed friction provides a physically grounded estimate of friction that does not require fitting to a known inundation extent, but which can be fitted if desired. Remote sensing offers the opportunity to map these friction coefficients relatively straightforwardly and for low cost. Inundation was predicted using the LISFLOOD‐FP model for a reach on the River Nene, UK. Friction coefficients were produced from land cover predicted from Landsat TM imagery using both ML and fuzzy c‐means classifiction. The elevetion data used were from combined contour and differential global positioning system (GPS) elevation data. Predicted inundation using spatially‐distributed and static friction were compared. Spatially‐distributed friction had the greatest effect on the timing of flood inundation, but a small effect on predicted inundation extent. The results indicate that spatially‐distributed friction should be considered where the timing of initial flooding (e.g. for early warning) is important. Copyright © 2007 John Wiley & Sons, Ltd.     

Quantification of braided river channel change using archival digital image analysis

Historical archives of grey‐scale river channel imagery are extensive. Here, we present and test a methodology to extract detailed quantitative topographic data from such imagery of sand‐bed rivers. Extracting elevation information from rivers is difficult as they are characterized by a low relative relief (<4 m); the area of interest may be spatially extensive (e.g. active channel widths >500 m in large braided rivers); the rate of change of surface elevation is generally low except in the vicinity of individual channel banks where the rate of change is very high; there is the complication that comes from inundation; and there may be an added complication caused by blockage of the field of view by vegetation. Here, we couple archival photogrammetric techniques with image processing methods and test these for quantification of sand‐bed braided river dynamics, illustrated for a 500 m wide, 3 km long reach of the South Saskatchewan River, Canada. Digital photogrammetry was used to quantify dry areas and water edge elevations. A methodology was then used to calibrate the spectral signature of inundated areas by combining established two media digital photogrammetric methods and image matching. This allowed determination of detailed depth maps for inundated areas and, when combined with dry area data, creation of complete digital elevation models. Error propagation methods were used to determine the erosion and deposition depths detectable from sequential digital elevation models. The result was a series of elevation models that demonstrate the potential for acquiring detailed and precise elevation data from any historical aerial imagery of rivers without needing associated calibration data, provided that imagery is of the necessary scale to capture the features of interest. We use these data to highlight several aspects of channel change on the South Saskatchewan River, including bar movement, bank erosion and channel infilling. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation of ASTER GDEM and SRTM and their suitability in hydraulic modelling of a glacial lake outburst flood in southeast Tibet

The resolution and accuracy of digital elevation models (DEMs) can affect the hydraulic simulation results for predicting the effects of glacial lake outburst floods (GLOFs). However, for the Tibetan Plateau, high‐quality DEM data are often not available, leaving researchers with near‐global, freely available DEMs, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and the Shuttle Radar Topography Mission data (SRTM) for hydraulic modelling. This study explores the suitability of these two freely available DEMs for hydraulic modelling of GLOFs. Our study focused on the flood plain of a potentially dangerous glacial lake in southeastern Tibet, to evaluate the elevation accuracy of ASTER GDEM and SRTM, and their suitability for hydraulic modelling of GLOFs. The elevation accuracies of ASTER GDEM and SRTM were first validated against field global position system (GPS) survey points, and then evaluated with reference to the relatively high precision of 1:50 000 scale DEM (DEM5) constructed from aerial photography. Moreover, the DEM5, ASTER GDEM and SRTM were used as basic topographic data to simulate peak discharge propagation, as well as flood inundation extent and depth in the Hydrologic Engineering Center's River Analysis System one‐dimensional hydraulic model. Results of the three DEM predictions were compared to evaluate the suitability of ASTER GDEM and SRTM for GLOF hydraulic modelling. Comparisons of ASTER GDEM and SRTM each with DEM5 in the flood plain area show root‐mean‐square errors between the former two as ± 15·4 m and between the latter two as ± 13·5 m. Although SRTM overestimates and ASTER GDEM underestimates valley floor elevations, both DEMs can be used to extract the elevations of required geometric data, i.e. stream centre lines, bank lines and cross sections, for flood modelling. However, small errors still exist in the cross sections that may influence the propagation of peak discharge. The flood inundation extent and mean water depths derived from ASTER GDEM predictions are only 2·2% larger and 2·3‐m deeper than that of the DEM5 predictions, whereas the SRTM yields a flood zone extent 6·8% larger than the DEM5 prediction and a mean water depth 2·4‐m shallower than the DEM5 prediction. The modelling shows that, in the absence of high‐precision DEM data, ASTER GDEM or SRTM DEM can be relied on for simulating extreme GLOFs in southeast Tibet. Copyright © 2011 John Wiley & Sons, Ltd.     

The 1997 eruption of Okmok Volcano, Alaska: a synthesis of remotely sensed imagery

Okmok Volcano, in the eastern Aleutian Islands, erupted in February and March of 1997 producing a 6-km-long lava flow and low-level ash plumes. This caldera is one of the most active in the Aleutian Arc, and is now the focus of international multidisciplinary studies. A synthesis of remotely sensed data (AirSAR, derived DEMs, Landsat MSS and ETM+ data, AVHRR, ERS, JERS, Radarsat) has given a sequence of events for the virtually unobserved 1997 eruption. Elevation data from the AirSAR sensor acquired in October 2000 over Okmok were used to create a 5-m resolution DEM mosaic of Okmok Volcano. AVHRR nighttime imagery has been analyzed between February 13 and April 11, 1997. Landsat imagery and SAR data recorded prior to and after the eruption allowed us to accurately determine the extent of the new flow. The flow was first observed on February 13 without precursory thermal anomalies. At this time, the flow was a large single lobe flowing north. According to AVHRR Band 3 and 4 radiance data and ground observations, the first lobe continued growing until mid to late March, while a second, smaller lobe began to form sometime between March 11 and 12. This is based on a jump in the thermal and volumetric flux determined from the imagery, and the physical size of the thermal anomalies. Total radiance values waned after March 26, indicating lava effusion had ended and a cooling crust was growing. The total area (8.9 km²), thickness (up to 50 m) and volume (1.54×10⁸ m³) of the new lava flow were determined by combining observations from SAR, Landsat ETM+, and AirSAR DEM data. While the first lobe of the flow ponded in a pre-eruption depression, our data suggest the second lobe was volume-limited. Remote sensing has become an integral part of the Alaska Volcano Observatory’s monitoring and hazard mitigation efforts. Studies like this allow access to remote volcanoes, and provide methods to monitor potentially dangerous ones.     

Remote sensing of river stage using the cross‐sectional inundation area‐river stage relationship (IARSR) constructed from digital elevation model data

Remote sensing of discharge and river stage from space provides us with a promising alternative approach to monitor watersheds, no matter if they are ungauged, poorly gauged, or fully gauged. One approach is to estimate river stage from satellite measured inundation area based on the inundation area – river stage relationship (IARSR). However, this approach is not easy to implement because of a lack of data for constructing the IARSR. In this study, an innovative and robust approach to construct the IARSR from digital elevation model (DEM) data was developed and tested. It was shown that the constructed IARSR from DEM data could be used to retrieve water level or river stage from satellite‐measured inundation area. To reduce the uncertainty in the estimated inundation area, a dual‐thresholding method was proposed. The first threshold is the lower limit of pixel value for classifying water body pixels with a relatively high‐level certainty. The second threshold is the upper limit of pixel value for classifying potentially flooded pixels. All pixels with values between the first threshold and the second threshold and adjacent to the classified water body pixels may be partially flooded. A linear interpolation method was used to estimate the wetted area of each partially flooded pixel. In applying the constructed IARSR to the estimated inundation areas from 11 Landsat TM images, 11 water levels were obtained. The root mean square error (RMSE) of the estimated water levels compared with the observed water levels at the US Geological Survey (USGS) gauging station on the Trinity River at Liberty in Liberty County, Texas, is about 0.38 m. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial and temporal patterns of snow accumulation and aerial ablation across the McMurdo Dry Valleys,Antarctica

Snow in the McMurdo Dry Valleys is a potential source of moisture for subnivian soils in a cold desert ecosystem. In a water‐limited environment, enhanced soil moisture is expected to provide more favourable conditions for subnivian soil communities. In addition, snow cover insulates the underlying soil from air temperature extremes. Quantifying the spatial and temporal patterns of seasonal snow accumulation and ablation is necessary to understand these dynamics. Repeat high‐resolution imagery acquired for the 2009–2010 austral summer was used to map the seasonal distribution of snow across Taylor and Wright valleys, Southern Victorialand, Antarctica. An edge detection algorithm was used to perform an object‐based classification of snow‐covered area. Coupled with topographic parameters obtained from a 30‐m digital elevation model, unique distribution patterns were characterized for five regions within the neighbouring valleys. Time lapses of snow distribution in each region provide insight into spatially variable aerial ablation rates (change in area of landscape covered by snow) across the region. A strong coastal to interior gradient of decreasing snow‐covered area was evident for both Taylor and Wright valleys. The surrounding regions of Lake Fryxell, Lake Hoare, Lake Bonney, Lake Brownworth, and Lake Vanda exhibited losses of snow‐covered area of 9.61 km² (?93%), 1.63 km² (?72%), 1.07 km² (?97%), 2.60 km² (?82%), and 0.25 km² (?96%), respectively, as measured from peak accumulation in October to mid‐January. Differences in aerial ablation rates within and across local regions suggest that both topographic variation and regional microclimates influence the ablation of seasonal snow cover. Copyright © 2012 John Wiley & Sons, Ltd.     

Using UAV acquired photography and structure from motion techniques for studying glacier landforms: application to the glacial flutes at Isfallsglaciären

Glacier and ice sheet retreat exposes freshly deglaciated terrain which often contains small‐scale fragile geomorphological features which could provide insight into subglacial or submarginal processes. Subaerial exposure results in potentially rapid landscape modification or even disappearance of the minor‐relief landforms as wind, weather, water and vegetation impact on the newly exposed surface. Ongoing retreat of many ice masses means there is a growing opportunity to obtain high resolution geospatial data from glacier forelands to aid in the understanding of recent subglacial and submarginal processes. Here we used an unmanned aerial vehicle to capture close‐range aerial photography of the foreland of Isfallsglaciären, a small polythermal glacier situated in Swedish Lapland. An orthophoto and a digital elevation model with ~2 cm horizontal resolution were created from this photography using structure from motion software. These geospatial data was used to create a geomorphological map of the foreland, documenting moraines, fans, channels and flutes. The unprecedented resolution of the data enabled us to derive morphological metrics (length, width and relief) of the smallest flutes, which is not possible with other data products normally used for glacial landform metrics mapping. The map and flute metrics compare well with previous studies, highlighting the potential of this technique for rapidly documenting glacier foreland geomorphology at an unprecedented scale and resolution. The vast majority of flutes were found to have an associated stoss‐side boulder, with the remainder having a likely explanation for boulder absence (burial or erosion). Furthermore, the size of this boulder was found to strongly correlate with the width and relief of the lee‐side flute. This is consistent with the lee‐side cavity infill model of flute formation. Whether this model is applicable to all flutes, or multiple mechanisms are required, awaits further study. © 2016 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Bayesian updating of flood inundation likelihoods conditioned on flood extent data

Previously we have detailed an application of the generalized likelihood uncertainty estimation (GLUE) procedure to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. This method was applied to two sites where a single consistent synoptic image of inundation extent was available to test the simulation performance of the method. In this paper, we extend this to examine the predictive performance of the method for a reach of the River Severn, west‐central England. Uniquely for this reach, consistent inundation images of two major floods have been acquired from spaceborne synthetic aperture radars, as well as a high‐resolution digital elevation model derived using laser altimetry. These data thus allow rigorous split sample testing of the previous GLUE application. To achieve this, Monte Carlo analyses of parameter uncertainty within the GLUE framework are conducted for a typical hydraulic model applied to each flood event. The best 10% of parameter sets identified in each analysis are then used to map uncertainty in flood extent predictions using the method previously proposed for both an independent validation data set and a design flood. Finally, methods for combining the likelihood information derived from each Monte Carlo ensemble are examined to determine whether this has the potential to reduce uncertainty in spatially distributed measures of flood risk for a design flood. The results show that for this reach and these events, the method previously established is able to produce sharply defined flood risk maps that compare well with observed inundation extent. More generally, we show that even single, poor‐quality inundation extent images are useful in constraining hydraulic model calibrations and that values of effective friction parameters are broadly stationary between the two events simulated, most probably reflecting their similar hydraulics. Copyright © 2004 John Wiley & Sons, Ltd.     

Snow process monitoring in montane forests with time‐lapse photography

A camera network with hourly resolution was used to monitor the complex snow processes in montane forest environments. We developed a semi‐automatic procedure to interpret snow depths from the digital images, which exhibited high consistency with manual measurements and station‐based recordings. To extract snow interception dynamics, six binary classification methods were compared. The MaxEntropy classifier demonstrated better performance than the other methods under conditions of varying illumination and was therefore selected as the method used for quantifying snow in tree canopies. Snow accumulation and ablation on the ground, as well as snow loading and unloading in the forest canopies, were investigated using snow parameters derived from the time‐lapse photography monitoring. The influences of meteorologic conditions, forest cover, and elevation on the snow processes were also considered. Time‐lapse photography proved to be an effective and low‐cost approach for collecting useful information on snow processes and facilitating the set‐up of hydrological models.     

Using structure‐from‐motion to create glacier DEMs and orthoimagery from historical terrestrial and oblique aerial imagery

Increased resolution and availability of remote sensing products, and advancements in small‐scale aerial drone systems, allows observations of glacial changes at unprecedented levels of detail. Software developments, such as structure‐from‐motion (SfM), now allow users an easy and efficient method to generate three‐dimensional (3D) models and orthoimages from aerial or terrestrial datasets. While these advancements show promise for current and future glacier monitoring, many regions still suffer a lack of observations from earlier time periods. We report on the use of SfM to extract spatial information from various historic imagery sources. We focus on three geographic regions, the European Alps, high Arctic Norway and the Nepal Himalayas. We used terrestrial field photographs from 1896, high oblique aerial photographs from 1936 and aerial handheld photographs from 1978 to generate digital elevation models (DEMs) and orthophotos of the Rhone glacier, Brøggerhalvøya and the lower Khumbu glacier, respectively. Our analysis shows that applying SfM to historic imagery can generate high quality models using only ground control points. Limited camera/orientation information was largely reproduced using self‐calibrated model data. Using these data, we calculated mean ground sampling distances across each site which demonstrates the high potential resolution of resulting models. Vertical errors for our models are ±5.4 m, ±5.2 m and ±3.3 m. Differencing shows similar patterns of thinning at lower Rhone (European Alps) and Brøggerhalvøya (Norway) glaciers, which have mean thinning rates of 0.31 m a^?1 (1896–2010) to 0.86 m a^?1 (1936–2010) respectively. On these clean ice glaciers thinning is highest in the terminus region and decreasing up‐glacier. In contrast to these glaciers, uneven topography, exposed ice‐cliffs and debris cover on the Khumbu glacier create a highly variable spatial distribution of thinning. The mean thinning rate for the Khumbu study area was found to be 0.54 ± 0.9 m a^?1 (1978–2015). Copyright © 2017 John Wiley & Sons, Ltd.     

Assessing the reliability of probabilistic flood inundation model predictions

An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event. Here, a LISFLOOD‐FP hydraulic model of an extreme (>1 in 1000 years) flood event in Cockermouth, UK, is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post‐peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilized; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition. Copyright © 2015 John Wiley & Sons, Ltd.