Reliability Of Simulated Discharges For Different Gauge Locations In A Semi Distributed Rainfall Runoff Model

In terms of flood forecasting in alpine environments, predictions at different gauges as well as sites with exposed infrastructure within the catchment are required. The used semi-distributed hydrological model HQsim combines runoff formation and surface runoff routines with an implemented channel routing for river reaches. This allows the estimation of discharges at selected channel segments. As a case study a large alpine catchment with a size of 890 km² is used. The uncertainty in the discharge prediction is investigated at three discharge gauges located along the main river. The basis of our experimental set-up are 15,000 samples describing the prior parameter distribution obtained by means of a Latin Hypercube sampling. Out of this, we calculated a Generalized Likelihood Uncertainty Estimation (GLUE) for the flood discharge at each gauging station. As informal likelihood a combination of different Nash Sutcliffe Efficiencies (NSE) is used covering summer season as well as flood periods containing peak discharges. Based on the behavioral parameter settings for each individual gauge, the model prediction distribution and their means for the remaining gauging stations are computed and analyzed

What can we learn from comparing glacio-hydrological models?

Glacio-hydrological models combine both glacier and catchment hydrology modeling and are used to assess the hydrological response of high-mountain glacierized catchments to climate change. To capture the uncertainties from these model combinations, it is essential to compare the outcomes of several model entities forced with the same climate projections. For the first time, we compare the results of two completely independent glacio-hydrological models: (i) HQsim-GEM and (ii) AMUNDSEN. In contrast to prevailing studies, we use distinct glacier models and glacier initialization times. At first glance, the results achieved for future glacier states and hydrological characteristics in the Rofenache catchment in ötztal Alps (Austria) appear to be similar and consistent, but a closer look reveals clear differences. What can be learned from this study is that low-complexity models can achieve higher accuracy in the calibration period. This is advantageous especially when data availability is weak, and priority is given to efficient computation time. Furthermore, the time and method of glacier initialization play an important role due to different data requirements. In essence, it is not possible to make conclusions about the model performance outside of the calibration period or more specifically in the future. Hence, similar to climate modeling, we suggest considering different modeling approaches when assessing future catchment discharge or glacier evolution. Especially when transferring the results to stakeholders, it is vital to transparently communicate the bandwidth of future states that come with all model results. © 2020 by the authors

Improved snow and runoff modelling of glacierized catchments for flood forecasting

Im Rahmen der Hochwasserprognose für den Tiroler Inn (HoPI) ist das Schnee und Eisschmelzmodell SES im Einsatz. Mit dem vollverteilten Energiebilanzmodell werden hydrologische Simulationen in den vergletscherten Kopfeinzugsgebieten der süd-westlichen Innzubringer Sanna, Fagge, Pitze, Ötztaler Ache und Sill durchgeführt. Die Schritte zur Kalibrierung der hydrologischen Modelle der vergletscherten Einzugsgebiete in Tirol werden beschrieben. Flächendeckende Schneehöhedaten werden mittels fluggestützter Laserscanns erhoben und in Karten des Schneewasseräquivalents von vier Einzugsgebieten umgewandelt. Die räumliche Variabilität der Schneedecke wird mit der neuen Datengrundlage untersucht und Parameter zur lateralen Schneeumverteilung abgeleitet. Die Wahl der verbesserten Modellparameter für die Hochwasserprognose, die im Rahmen von Monte Carlo Simulationen variiert wurden, basiert auf Erkenntnissen zur Güte des Abfluss, der schneebedeckte Fläche und des flächendeckenden Schneewasseräquivalents. Verglichen mit Schneeflächendaten wird der höhere Nutzen der Wasseräquivalentsdaten für die Kalibrierung gezeigt. Die physikalische Konsistenz der verbesserten Schnee- und Abflussmodelle wird zusätzlich anhand von Fallstudien bewiesen, wobei weitere Messdaten zur Albedo von Schnee und zur Gletschermassenbilanz verwendet werden. Die Analyse von „Regen auf Schnee“ Ereignissen zeigt, dass die Bedeutung des Einzugsgebietszustandes Schneewasseräquivalent für die Simulation von vergangenen Hochwässern und Bemessungshochwässern in vergletscherten Einzugsgebieten wesentlich ist.In the framework of the flood forecasting system of the Tyrolean Inn River (HoPI) the snow and ice melt model SES is used for operational runoff forecasts of the glacierized headwater catchments. In the present thesis the calibration of the spatially-distributed energy balance model is described. Spatially-distributed snow depth data is extracted from airborne laser scanning data and transferred in patterns of snow water equivalent (SWE) of four catchments. The spatially distributed patterns of SWE help to apply appropriate input precipitation, to assess reliable parameterisations of snow redistribution and reduce the parameter uncertainty of the SES-model. Furthermore, it demonstrates that using SWE is superior to snow covered area for constraining parameter ranges of a snow model in a meso-scale catchment. The transferability of the models to ungauged catchments is verified. Parameter sets inferred by Monte Carlo simulations prove their physically consistency in case studies which focus on glacier mass balance and snow albedo. In terms of flood forecasting it is shown that parameter sets, which were optimised by SCA, SWE and runoff are less uncertain than parameter sets solely fitted to runoff data. Especially simulations of rain-on-snow events benefit of the improved snow simulations. Overall, the model performance of the hydrological models of the glacierized headwaters is improved.Johannes SchöberAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersEnth. u.a. 5 Veröff. d. Verf. aus den Jahren 2009 - 2012 . - Zsfassung in dt. SpracheInnsbruck, Univ., Diss., 2014OeBB(VLID)16537

TLS based snow covered area maps of the Weisssee snow research site (Kaunertal, Austria)

The data set comprises an inter- and intra-annual timeseries of ten high-resolution (0.5 x 0.5 m) binary snow covered area (SCA) maps derived from TLS scans at the Weisssee Snow Research Site in Austria between March 2017 and November 2019. TLS based digital elevation models and difference (snow depth) grids can be downloaded as a separate dataset (Fey et al., 2018; https://doi.org/10.1594/PANGAEA.896843). The binary classification of snow-covered and snow-free areas is based on intensity and snow depth. An intensity threshold of 3000 was defined based on histogram analysis in patchy snowpack conditions. Snow-covered areas were delineated according to TLS based snow depth information. Snow-depth related classifications were based on a threshold value representing the precision of the TLS acquisition represented by the standard deviation of snow-free surfaces (see Fey et al., 2019). The resulting classification was validated with fully snow covered scenes. For the scene of 2017-05-07 two available TLS scans, one with a Riegl VZ-4000 and another with a Riegl VZ-6000 scanner, were combined into one snow covered area map. This was done due to the fact that the VZ-4000 data is better suited for snow cover discrimination based on intensity data, while not providing data on wet snow surfaces in larger distance where the VZ-6000 scanner still provides snow depth observations. The overall coverage of the scan area is identical to the one of the DGM dataset. The SCA dataset comprises three classes: snow-free (0), snow-covered (1) and NoData (-99999). No data areas are caused by obstacles in the field-of-view of the laserscanner. The SCA data can be used for validating remote sensing products including fractional snow coverage from e.g. Landsat and Sentinel-2 as done in the related literature

TLS snow distribution maps of the Weisssee snow research site (Kaunertal, Austria)

The data set comprises the inter- and intra-annual snow depth distribution recorded by TLS scans at Weisssee snow research site in Austria between November 2014 and May 2018. The data set comprises 23 snow-on digital elevation models (DEM), one snow-off DEM, and the difference raster calculated between a snow-off and snow-on scans. The relative accuracy of the TLS scans was determined by measuring the distance between snow-free planes from the snow-on and snow-off scans and shows mean values smaller than 0.03 m and standard deviations ranging between 0.02 and 0.1 m. The reliability of the snow depths derived from TLS was further assessed by comparing snow depths from snow probing, GNSS measurements, and continuous snow depth measurements from the weather station. Comparison of the different measurement methods shows average deviations of less than 0.1 m. The data can be used for analysing snow distributions, or for assessing the representativeness of conventional snow depth sensors. Other use cases include assessing other in-situ sensors like Cosmic-Ray-Neutron Sensors, or space-borne snow-covered area products. More details are described in an article submitted to the Water Resources Research Special Issue: Advances in remote sensing, measurement, and simulation of seasonal snow