COSMOS-Europe : a European network of cosmic-ray neutron soil moisture sensors

We thank TERENO (Terrestrial Environmental Observatories), funded by the Helmholtz-Gemeinschaft for the financing and maintenance of CRNS stations. We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) of the research unit FOR 2694 Cosmic Sense (grant no. 357874777) and by the German Federal Ministry of Education of the Research BioökonomieREVIER, Digitales Geosystem – Rheinisches Revier project (grant no. 031B0918A). COSMOS-UK has been supported financially by the UK’s Natural Environment Research Council (grant no. NE/R016429/1). The Olocau experimental watershed is partially supported by the Spanish Ministry of Science and Innovation through the research project TETISCHANGE (grant no. RTI2018-093717-BI00). The Calderona experimental site is partially supported by the Spanish Ministry of Science and Innovation through the research projects CEHYRFO-MED (grant no. CGL2017-86839- C3-2-R) and SILVADAPT.NET (grant no. RED2018-102719-T) and the LIFE project RESILIENT FORESTS (grant no. LIFE17 CCA/ES/000063). The University of Bristol’s Sheepdrove sites have been supported by the UK’s Natural Environment Research Council through a number of projects (grant nos. NE/M003086/1, NE/R004897/1, and NE/T005645/1) and by the International Atomic Energy Agency of the United Nations (grant no. CRP D12014). Acknowledgements. We thank Peter Strauss and Gerhab Rab from the Institute for Land and Water Management Research, Federal Agency for Water Management Austria, Petzenkirchen, Austria. We thank Trenton Franz from the School of Natural Resources, University of Nebraska–Lincoln, Lincoln, NE, United States. We also thank Carmen Zengerle, Mandy Kasner, Felix Pohl, and Solveig Landmark, UFZ Leipzig, for supporting field calibration, lab analysis, and data processing. We furthermore thank Daniel Dolfus, Marius Schmidt, Ansgar Weuthen, and Bernd Schilling, Forschungszentrum Jülich, Germany. The COSMOS-UK project team is thanked for making its data available to COSMOS-Europe. Luca Stevanato is thanked for the technical details about the Finapp sensor. The stations at Cunnersdorf, Lindenberg, and Harzgerode have been supported by Falk Böttcher, Frank Beyrich, and Petra Fude, German Weather Service (DWD). The Zerbst site has been supported by Getec Green Energy GmbH and Jörg Kachelmann (Meteologix AG). The CESBIO sites have been supported by the CNES TOSCA program. The ERA5-Land data are provided by ECMWF (Muñoz Sabater, 2021). The Jena dataset was retrieved at the site of The Jena Experiment, operated by DFG research unit FOR 1451.Peer reviewedPublisher PD

Glacier outlines in the Ala Archa valley, Kyrgyzstan between 1968-2017

The inventory contains shapefiles of the glacier area in the Ala Archa valley for the years 1968, 1975, 1994, 2010 and 2017. The Ala Archa valley lies in the Kyrgyz Ala-Too (Kyrgyz range) which represents the north-western part of the Tien Shan. The area is very important for scientific research and the freshwater supply for the greater Bishkek region, as it lies in the vicinity of Bishkek, the capital of Kyrgyzstan. The Mapping of the glaciers was conducted based on declassified satellite imagery obtained from the USGS for the days 19/08/1968, 11/07/1975 and Landsat imagery for the days 01/09/1994, 28/08/2010 and 31/08/2017. The satellite images were carefully selected in order to find cloud cover-free images in the summer months (July-Aug-Sept). The classification of the glaciers is based on the normalized difference snow index (NDSI) method and was enhanced manually

Cosmic-Ray Neutron Sensing and Snow Covered Area as Complementary Information Sources for Robust Water Resource Management in Alpine Terrain

In vielen Regionen stellt saisonal in Form von Schnee gespeichertes Wasser eine wichtige Komponente des Wasserkreislaufs dar. Ein erheblicher Teil der Weltbevölkerung ist auf diese nicht nur angesichts des Klimawandels unter Druck stehende Ressource angewiesen. Das Wissen um die räumliche und zeitliche Dynamik der Schneedecke ist für eine Verbesserung des Wasserresourcenmanagements und der Hochwasservorhersage in Schnee beeinflussten Einzugsgebieten unverzichtbar. Zur Abschätzung der in der Schneedecke gespeicherten Wassermenge stehen verschiedene Ansätze wie In-situ-Messungen, Fernerkundung und schneehydrologische Modellierung zur Verfügung. Insbesondere in Gebirgsräumen weisen die einzelnen Ansätze jeweils spezifische Vor- und Nachteile auf. Das grundlegende Konzept dieser Arbeit basiert daher auf der Annahme, dass die Kombination komplementärer Informationsquellen einen Mehrwert gegenüber den einzelnen Komponenten darstellt. So sind beispielsweise konventionelle In-situ Messungen anfällig für lokale Anomalien. Aus diesem Grund wird eine Pilotstudie am Weisssee (Kaunertal, österreichische Alpen) durchgeführt. Darin wird Cosmic-Ray Neutron Sensing, eine auf der Messung kosmogener Neutronen in der bodennahen Atmosphähre basierende Methode deren Signal die Schneeverhältnisse in einem größeren Umkreis reflektiert, getestet. Die dabei gewonnen Messdaten werden in Kombination mit Satelliten gestützen Schneebedeckungskarten zur Kalibrierung eines weiterentwickelten hydrologischen Modells in einem Teileinzugsgebiet des Inns verwendet. Neutronensimulationen geben weitere Einblicke in die Charakteristik dieser Messsensorik. Die Ergebnisse verdeutlichen die Vorteile gegenüber konventionellen Methoden, insbesondere in Bezug auf die räumliche Repräsentativität. Ein Wasseräquivalent von bis zu 600 mm konnte gemessen werden. Die empirischen Ergebnisse werden von den Neutronensimulationen gestützt. Einschränkend ist jedoch zu Erwähnen, dass beide Ansätze erhebliche Unsicherheiten oberhalb einer Schwelle von 400 mm aufzeigen. Dessen ungeachtet bestätigt sich ein erheblicher Mehrwert des kombinierten Ansatzes aus repräsentativer In-situ Messung und optischen Satellitendaten zur Reduzierung der Unsicherheiten des hydrologischen Modells. Mögliche zukünftige Forschungsfragen beinhalten (i) die Übertragbarkeit der Ergebnisse auf andere Standorte und (ii) Ansätze zur Reduzierung der Messunsicherheiten auch oberhalb des in dieser Arbeit gefundenen Schwellenwertes von 400 mm. Darüber hinaus ist vorgesehen, die ermutigenden Ergebnisse dieser Pilotstudie in der operationellen Hochwasservorhersage für den Inn (HoPI) zu implementieren. Weiters soll der Ansatz mit dem Ziel robusterer Klimafolgenabschätzungen auf Regionen mit geringer Messnetzdichte ausgeweitet werden.For many environments, snow is an important part of the hydrolocigal cycle. A substantial portion of the planet's population relies on freshwater resources seasonally stored as snow. Not only in the face of climate change, these resources are increasingly under pressure. The knowledge of the spatial and temporal dynamics of snow is therefore crucial for improved water resource management and flood forecasting in snow-fed river basins. A number of approaches exist to monitor snow water equivalent including in-situ measurements, remote sensing, and hydrological modelling. When applied separately, both specific advantages and drawbacks exist for each of these, in particular in mountain regions. It is thus assumed, that there is an added-value in integrating complementary sources of information. Hypothesizing that conventional in-situ techniques for continuous measurements of snow water equivalent are prone to local anomalies, a proof-of-concept study utilising a novel approach based on cosmic-ray neutron sensing is tested at a field site in the Austrian Alps at lake Weisssee, Kaunertal. The evaluation of both the representativeness of conventional sensors and cosmic-ray neutron sensing is based on a set of inter- and intra-annual terrestrial laser scanning campaigns. The key benefit of aboveground cosmic-ray neutron sensing is its intermediate scale footprint being insensitive to local variations in snow accumulation. Combining this technique with remotely sensed snow covered area information, a further developed snow hydrological model is calibrated for a tributary catchment of the Inn River. Neutron modelling simulations give further insights into the characteristics of continuous snow measurements. The results clearly show the advantages of cosmic-ray neutron sensing over conventional measurements, in particular with regard to spatial representativeness. The technique is shown to be capable of measuring up to 600 mm of water equivalence, though both empirical results and neutron modelling exhibit substantial uncertainties above a threshold of 400 mm. In total, neutron modelling confirms the unexpectedly high suitability for measuring even deep snowpacks. Furthermore, a considerable benefit of the combined approach integrating cosmic-ray neutron sensing and optical satellite imagery for reducing the uncertainties of the snow hydrological model is shown. Possible further research questions include (i) the transferability of the results to other environments and (ii) approaches to reduce the measurement uncertainties of cosmic-ray neutron sensing even beyond the threshold of 400 mm found in this work. Moreover, it is foreseen to implement the encouraging results of this proof-of-concept study into the operational flood forecasting for the Inn River (HoPI) and to expand the approach to regions with sparse measurement networks for improving the robustness of climate change impact assessments.Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersArbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftInnsbruck, Univ., Diss., 2019(VLID)363100

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

Cosmic-Ray Neutron Data at the Weisssee Snow Research Site

Cosmic-Ray Neutron data for the period 2014/02/25 to 2016/06/29. The Hydroinnova CRS1000 detector was placed approximately 2.5 m above snow-free ground. All neutron count data are normalized to the second device used from 10/2014 but are not corrected or processed otherwise. Auxiliary data include relative humidity and air pressure recorded by the sensor. Further meteorological data can be found in Schöber et al. (2019). The NMDB Nest application (http://www.nmdb.eu/nest/) can be used to download neutron monitor data for the incoming neutron correction. The corrections needed to account for atmospheric and incoming neutron effects are described in Zreda et al. (2012, doi:10.5194/hess-16-4079-2012). Further information regarding the location and the use of the data to derive snow water equivalent is found in Schattan et al. (2017)

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