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Spectral fingerprinting
(2015)
Current research on runoff and erosion processes, as well as an increasing demand for sustainable watershed management emphasize the need for an improved understanding of sediment dynamics. This involves the accurate assessment of erosion rates and sediment transfer, yield and origin. A variety of methods exist to capture these processes at the catchment scale. Among these, sediment fingerprinting, a technique to trace back the origin of sediment, has attracted increasing attention by the scientific community in recent years. It is a two-step procedure, based on the fundamental assumptions that potential sources of sediment can be reliably discriminated based on a set of characteristic ‘fingerprint’ properties, and that a comparison of source and sediment fingerprints allows to quantify the relative contribution of each source.
This thesis aims at further assessing the potential of spectroscopy to assist and improve the sediment fingerprinting technique. Specifically, this work focuses on (1) whether potential sediment sources can be reliably identified based on spectral features (‘fingerprints’), whether (2) these spectral fingerprints permit the quantification of relative source contribution, and whether (3) in situ derived source information is sufficient for this purpose. Furthermore, sediment fingerprinting using spectral information is applied in a study catchment to (4) identify major sources and observe how relative source contributions change between and within individual flood events. And finally, (5) spectral fingerprinting results are compared and combined with simultaneous sediment flux measurements to study sediment origin, transport and storage behaviour.
For the sediment fingerprinting approach, soil samples were collected from potential sediment sources within the Isábena catchment, a meso-scale basin in the central Spanish Pyrenees. Undisturbed samples of the upper soil layer were measured in situ using an ASD spectroradiometer and subsequently sampled for measurements in the laboratory. Suspended sediment was sampled automatically by means of ISCO samplers at the catchment as well as at the five major subcatchment outlets during flood events, and stored fine sediment from the channel bed was collected from 14 cross-sections along the main river. Artificial mixtures of known contributions were produced from source soil samples. Then, all source, sediment and mixture samples were dried and spectrally measured in the laboratory. Subsequently, colour coefficients and physically based features with relation to organic carbon, iron oxide, clay content and carbonate, were calculated from all in situ and laboratory spectra. Spectral parameters passing a number of prerequisite tests were submitted to principal component analyses to study natural clustering of samples, discriminant function analyses to observe source differentiation accuracy, and a mixing model for source contribution assessment. In addition, annual as well as flood event based suspended sediment fluxes from the catchment and its subcatchments were calculated from rainfall, water discharge and suspended sediment concentration measurements using rating curves and Quantile Regression Forests. Results of sediment flux monitoring were interpreted individually with respect to storage behaviour, compared to fingerprinting source ascriptions and combined with fingerprinting to assess their joint explanatory potential.
In response to the key questions of this work, (1) three source types (land use) and five spatial sources (subcatchments) could be reliably discriminated based on spectral fingerprints. The artificial mixture experiment revealed that while (2) laboratory parameters permitted source contribution assessment, (3) the use of in situ derived information was insufficient. Apparently, high discrimination accuracy does not necessarily imply good quantification results. When applied to suspended sediment samples of the catchment outlet, the spectral fingerprinting approach was able to (4) quantify the major sediment sources: badlands and the Villacarli subcatchment, respectively, were identified as main contributors, which is consistent with field observations and previous studies. Thereby, source contribution was found to vary both, within and between individual flood events. Also sediment flux was found to vary considerably, annually as well as seasonally and on flood event base. Storage was confirmed to play an important role in the sediment dynamics of the studied catchment, whereas floods with lower total sediment yield tend to deposit and floods with higher yield rather remove material from the channel bed. Finally, a comparison of flux measurements with fingerprinting results highlighted the fact that (5) immediate transport from sources to the catchment outlet cannot be assumed. A combination of the two methods revealed different aspects of sediment dynamics that none of the techniques could have uncovered individually.
In summary, spectral properties provide a fast, non-destructive, and cost-efficient means to discriminate and quantify sediment sources, whereas, unfortunately, straight-forward in situ collected source information is insufficient for the approach. Mixture modelling using artificial mixtures permits valuable insights into the capabilities and limitations of the method and similar experiments are strongly recommended to be performed in the future. Furthermore, a combination of techniques such as e.g. (spectral) sediment fingerprinting and sediment flux monitoring can provide comprehensive understanding of sediment dynamics.
Water resources from Central Asia’s mountain regions have a high relevance for the water supply of the water scarce lowlands. A good understanding of the water cycle in these mountain regions is therefore needed to develop water management strategies. Hydrological modeling helps to improve our knowledge of the regional water cycle, and it can be used to gain a better understanding of past changes or estimate future hydrologic changes in view of projected changes in climate. However, due to the scarcity of hydrometeorological data, hydrological modeling for mountain regions in Central Asia involves large uncertainties.
Addressing this problem, the first aim of this thesis was to develop hydrological modeling approaches that can increase the credibility of hydrological models in data sparse mountain regions. This was achieved by using additional data from remote sensing and atmospheric modeling. It was investigated whether spatial patterns from downscaled reanalysis data can be used for the interpolation of station-based precipitation data. This approach was compared to other precipitation estimates using a hydrologic evaluation based on hydrological modeling and a comparison of simulated and observed discharge, which demonstrated a generally good performance of this method. The study further investigated the value of satellite-derived snow cover data for model calibration. Trade-offs of good model performance in terms of discharge and snow cover were explicitly evaluated using a multiobjective optimization algorithm, and the results were contrasted with single-objective calibration and Monte Carlo simulations. The study clearly shows that the additional use of snow cover data improved the internal consistency of the hydrological model. In this context, it was further investigated for the first time how many snow cover scenes were required for hydrological model calibration.
The second aim of this thesis was the application of the hydrological model in order to investigate the causes of observed streamflow increases in two headwater catchments of the Tarim River over the recent decades. This simulation-based approach for trend attribution was complemented by a data-based approach. The hydrological model was calibrated to discharge and glacier mass balance data and considered changes in glacier geometry over time. The results show that in the catchment with a lower glacierization, increasing precipitation and temperature both contributed to the streamflow increases, while in the catchment with a stronger glacierization, increasing temperatures were identified as the dominant driver.