ALBERT

Description: The Seti Khola (=river) runs along one of the steepest topographic gradients in the Central Himalayas and is the main drainage system of the Pokhara Valley, home to the eponymous city with an estimated population of half a million. In the Pokhara Valley, the Seti Khola runs through a distinct landscape dominated by broad, unpaired, alluvial terraces which abruptly alternate with short (〈1 km) reaches, where the river flows through narrow (〈10 m) and deep (up to 90 m) gorges. In order to facilitate hydrodynamic modelling of one-dimensional, steady flow in HEC-RAS 5.0.7, we surveyed the Seti Khola's channel and overbank topography as well as surface roughness along a 30-km long reach. During two field-visits (October 2016 and October 2019), we surveyed a total of 95 river cross sections utilising a TruPulse 360 laser range finder and a Garmin eTrex handheld GPS. Additionally, during our October 2019 field-season, we also estimated surface roughness or Manning's n of the Seti Khola's channel and left and right overbank at 61 locations –using the determination methodology described by Arcement Jr and Schneider (1984; doi:10.3133/wsp2339) and Chow (1959).

Description: Vast parts of Arctic Siberia are underlain by ice-rich permafrost, which is exposed to different processes of degradation due to global warming. Thermal erosion as a key process for landscape degradation in these regions causes the recent reactivation and formation of new landforms like thermo-erosional valleys and gullies. However, a statistical assessment about the decisive factors and the locations most susceptible to this phenomenon is still missing. We investigated the influence of different environmental parameters on the occurrence of recently observed thermal erosion using a GIS-based approach and statistical modeling by logistic regression. The study site is located on an island within the Arctic Lena River Delta and is mainly composed of ice- and organic-rich deposits of the Yedomatype Ice Complex. Field surveys and mapping on the basis of high-resolution remotely sensed data revealed that thermal erosion occurs predominantly i) on very steep slopes along the margins of the island, ii) in the upper reaches of deeply incised valleys and iii) in gullies. In order to detect the regulation factors for those thermo-erosional landforms, we derived several environmental parameters using a high-resolution DEM and satellite imagery. We chose a stepwise logistic regression approach to reduce the full set of potential parameters. This approach allowed the selection of a parsimonious model, i.e. a best-fit model using as few parameters as possible. The parameters Contribution of warm open surface water, Relief ratio, Direct solar radiation and Snow accumulation turned out to be the decisive factors for thermal erosion. Uncertainties in the model due to sampling and model selection were valuated both statistically and spatially through the generation of 100 models. Receiver Operating Characteristics (ROCs) were used to validate the spatial predictive capability of each model run. The consensus map as the median of all 100 susceptibility models represents the final susceptibility map. The agreement between mapped and predicted erosion turned out to be generally very high within the study site, confirmed by an Area under the ROC curve (AUC) of 0.957 for the consensus map. The variability of predicted erosion probabilities between the single models is about four percentage points per cell within the study site and thus, very low. We attributed the slight mismatches between observed and predicted erosion to the generation of the explanatory environmental parameters and the modeling approach. Model results seem promising for the spatial prediction of susceptible sites for thermal erosion and, thus, could be a tool to explain the geomorphic forming in this rapidly changing environment. As these results are based on a single case study, future investigation should focus on the transferability of the model by applying an external validation on other sites with comparable environmental conditions.

Description: Возросший научный интерес к деградации ледового комплекса в последнее время вызван актуальностью вопросов климатических изменений. Высокая уязвимость многолетнемерзлых отложений ледового комплекса связана с высоким содержанием в нем льда и лабильных органических веществ, что при потеплении климата, может привести к высвобождению парниковых газов в атмосферу. Термокарст и термоэрозия – два основных типа деградации многолетнемерзлых пород арктических равнин, и в частности регионов распространения ледового комплекса. Эти процессы и формирующиеся в результате формы рельефа способствуют высвобождению органических веществ в атмосферу и в гидросферу, а также могут оказать существенное влияние на водные и энергетические балансы подверженных их влиянию ландшафтов. В то время как термокарстовые процессы широко изучены, процессы термоэрозии исследованы недостаточно, несмотря на то, что соответствующие им формы рельефа, такие как термоэрозионные овраги, долины и долинные сети, широко распространены в регионе ледового комплекса. Нами было исследовано 1) воздействие термоэрозионных процессов на трансформацию рельефа в регионе моря Лаптевых с начала голоцена и 2) интенсивность современных термоэрозионных процессов и развитие форм рельефа в дельте р. Лены. Исследование и описание термоэрозионных форм рельефа на региональном уровне с использованием ГИС-анализа данных дистанционного зондирования, цифровых моделей рельефа и полевых исследований показали, что в Голоцене в некоторых частях дельты термоэрозия оказывала сильное влияние на деградацию ледового комплекса и гораздо более значительное, чем термокарст. Значительные различия в морфологии и пространственном распределении водотоков и термоэрозионных оврагов наблюдались между различными районами ледового комплекса, что связано с разницей размеров исследуемых районов, их рельефом и преобладающими криолитологическими свойствами, а также со степенью предыдущей деградации ледового комплекса термокарстом. Сравнение спутниковых снимков, полученных в период с 1964 по 2011 год показывает увеличение длины термоэрозионных водотоков и долин в пределах ледового комплекса дельты р. Лены на 1,6 %. При этом интенсивность современных термоэрозионных процессов сильно отличается в различных частях дельты, а также в течение различных промежутков времени.

Description: Vast parts of Arctic Siberia are underlain by ice-rich permafrost, which is exposed to different processes of degradation due to global warming. Thermal erosion as a key process for landscape degradation causes the recent reactivation and formation of new landforms like thermoerosional valleys and gullies. However, a statistical assessment about the decisive factors and the locations most susceptible for this phenomenon is still missing. This study investigates the influence of different geomorphological parameters on the occurrence of recently observed thermal erosion using a GIS-based approach and statistical modeling by logistic regression. The study site is located on an island within the Arctic Lena River Delta and is mainly composed of ice- and organic-rich deposits. Field surveys and mapping of high-resolution remotely sensed data revealed that thermal erosion occurs predominantly i) on very steep slopes along the margins of the island, ii) in the upper reaches of deeply incised thermo-erosional valleys and iii) in thermo-erosional gullies. Several potentially influencing environmental parameters were derived by a combination of high-resolution satellite imagery and 2 m-DEM. The full set of parameters was reduced stepwise within the logistic regression model. This approach allows the selection of a parsimonious model, i.e. a best-fit model using as few variables as possible. The parameters Contribution of warm open surface water, Relief ratio, Direct solar radiation and Snow accumulation turned out be the decisive factors for thermal erosion. Uncertainties in the model due to sampling and model selection were evaluated statistically and spatially through the generation of 100 models. Receiver Operating Characteristics (ROCs) were used to validate the spatial predictive capability of each model run. The consensus map as the median of all susceptibility models represents the final susceptibility map. The agreement between mapped and predicted erosion is generally very high within the study site, confirmed by an Area under the ROC curve (AUC) of 0.957 for the consensus map. The variability of predicted erosion probabilities between the single models is about four percentage points per cell within the study site and thus, very low. Mismatches between observed and predicted erosion could be attributed to the generation of the explanatory environmental parameters and the modeling approach. Model results seem promising for the spatial prediction of susceptible sites for thermal erosion, but require external validation on other sites with comparable environmental conditions.

Description: Methane fluxes on an active flood plain situated in the Siberian Lena River Delta were studied applying the eddy covariance method. During the growing season, the observed fluxes exhibited a great deal of temporal variability, which was largely the result of the pronounced spatial variability of soil and vegetation characteristics within the footprint. Explaining this variability was based on three data-driven modelling approaches: the automatically operating algorithms stepwise regression as well as neural network, and a mechanistic model, which utilised exponential relationships between the methane flux and both flux drivers soil temperature and friction velocity. A substantial improvement in model performance was achieved by applying footprint information in the form of relative contributions of three vegetation classes to the flux signal. This aspect indicates that the vegetation served as an integrated proxy for flux drivers, whose characteristics permanently varied according to the shifting source area. The neural network performed best in explaining the variability of the observed methane fluxes. However, validating the models’ generalisability revealed that the mechanistic model provided the most predictive power suggesting that this model best captured the causality between the methane flux and its drivers. After integrating the gap-filled time series, all models yielded footprint budgets that were similar in magnitude. These budgets, however, lacked representativity due to the sensor location bias, i.e. their strong dependence on tower location, measurement height and wind field conditions. Thus, an unbiased budget of the total area of the flood plain was estimated utilising the mechanistic model. Initially, a downscaling procedure partitioned the observed flux with a seasonal mean of 0.012 μmol m-2 s-1 into three individual vegetation class fluxes accounting for shrubs (0.0004 μmol m-2 s-1), sedges (0.052 μmol m-2 s-1) and intermediate vegetation (0.018 μmol m-2 s-1). These decomposed fluxes in turn formed the basis – in conjunction with a classified high-resolution orthomosaic of the flood plain – for the vegetation class area-weighted upscaling. Alternatively, the straightforward upscaling of the footprint budgets (without the preceding downscaling) yielded budgets that underestimated the methane source strength of the flood plain by roughly 42 %. Hence, the application of fine-scale information on surface characteristics is crucial for both modelling methane flux dynamics and adequately estimating budgets of heterogeneous ecosystems being abundant in the tundra biome.