Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Schattan, Paul; Baroni, Gabriele; Oswald, Sascha E.; Schöber, Johannes; Fey, Christine; Kormann, Christoph; Huttenlau, Matthias; Achleitner, Stefan

doi:10.1002/2016wr020234

Cited by 96 publications

(112 citation statements)

References 92 publications

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“…During the agricultural season in 2014, Scheiffele (2015) used the COSMOS standard sampling scheme for three calibration campaigns in May (very small crop, mediocre soil moisture), July (maximum water content in biomass, dry soil), and October (biomass residues after harvest, mediocre soil moisture). The general behavior of the soil moisture dynamics could be reproduced well (Fig.…”

Section: Improvement Of the Calibration Performancementioning

confidence: 99%

“…Heidbüchel et al (2016) were the first to test the impact of the revised function on the performance of their calibration data. Schattan et al (2017) applied the revised weighting approach to average complex snow patterns in an alpine terrain. Encouraged by both of their promising results, the present study has hypothesized that this new theory could enable an improved performance of CRNS calibration and validation campaigns for a huge variety of sites and conditions.…”

Section: Introductionmentioning

confidence: 99%

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Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Schrön

Köhli

Scheiffele

et al. 2017

Hydrol. Earth Syst. Sci.

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127

231

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Abstract. In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.

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Section: Improvement Of the Calibration Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Schrön

Köhli

Scheiffele

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

127

231

View full text Add to dashboard Cite

show abstract

“…Schattan et al, 2017), and thus, other long-term data series such as e.g. from Col de Porte ) may be investigated with a similar approach in future.…”

mentioning

confidence: 99%

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Hartl¹,

Koch²,

Marty³

et al. 2017

Preprint

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Abstract. The density of new snow is sometimes monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and now-casting 10 applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in-situ measurements.In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. 15Considering the settling of the new snow and the old snow pack, the average hourly new snow density is 68 kgm -3 with a standard deviation of 9 kgm -3 . Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the 20 investigated parameterizations, but relationships between new snow density and wet bulb temperature are partly visible in the automated measurements data. Wind speed is a crucial parameter for the inter-station variability of new snow density, with higher new snow density at more windy locations. Whereas snow measurements using ultrasonic devices and snow pillows are appropriate for calculating station mean new snow densities, we recommend instruments with higher accuracy e.g. optical devices for better investigations of the variability of new snow densities on sub daily intervals. 25Hydrol. Earth Syst. Sci. Discuss., https://doi

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“…Recent studies present the performance of cosmic ray neutron sensors (e.g. Schattan et al, 2017), which are partly used for long-term observations such as e.g. from Col de Porte .…”

Section: Introductionmentioning

confidence: 99%

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Hartl

Koch

Marty³

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The density of new snow is operationally monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and nowcasting applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in situ measurements. In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. Considering the settling of the new snow and the old snowpack, the average hourly new snow density is 68 kg m −3 , with a standard deviation of 9 kg m −3 . Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the investigated parameterizations. Applying simple linear regressions between new snow density and wet bulb temperature based on the measurements' data resulted in significant relationships (r 2 > 0.5 and p ≤ 0.05) for single periods at individual stations only. Higher new snow density was calculated for the highest elevated and most wind-exposed station location. Whereas snow measurements using ultrasonic devices and snow pillows are appropriate for calculating station mean new snow densities, we recommend instruments with higher accuracy e.g. optical devices for more reliable investigations of the variability of new snow densities at sub-daily intervals.

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Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Cited by 96 publications

References 92 publications

Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Obtaining sub-daily new snow density from automated measurements in high mountain regions

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