Monitoring soil moisture from middle to high elevation in Switzerland: set-up and first results from  the SOMOMOUNT network

Abstract. Besides its important role in the energy and water balance at the soil–atmosphere interface, soil moisture can be a particular important factor in mountain environments since it influences the amount of freezing and thawing in the subsurface and can affect the stability of slopes. In spite of its importance, the technical challenges and its strong spatial variability usually prevents soil moisture from being measured operationally at high and/or middle altitudes. This study describes the new Swiss soil moisture monitoring network SOMOMOUNT (soil moisture in mountainous terrain) launched in 2013. It consists of six entirely automated soil moisture stations distributed along an altitudinal gradient between the Jura Mountains and the Swiss Alps, ranging from 1205 to 3410 m a.s.l. in elevation. In addition to the standard instrumentation comprising frequency domain sensor and time domain reflectometry (TDR) sensors along vertical profiles, soil probes and meteorological data are available at each station. In this contribution we present a detailed description of the SOMOMOUNT instrumentation and calibration procedures. Additionally, the liquid soil moisture (LSM) data collected during the first 3 years of the project are discussed with regard to their soil type and climate dependency as well as their altitudinal distribution. The observed elevation dependency of LSM is found to be non-linear, with an increase of the mean annual values up to ∼  2000 m a.s.l. followed by a decreasing trend towards higher elevations. This altitude threshold marks the change between precipitation-/evaporation-controlled and frost-affected LSM regimes. The former is characterized by high LSM throughout the year and minimum values in summer, whereas the latter typically exhibits long-lasting winter minimum LSM values and high variability during the summer.

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Monitoring soil moisture from middle to high elevation in Switzerland: Set-up and first results from the SOMOMOUNT network

10.5194/hess-2016-474 ◽

2016 ◽

Cited By ~ 1

Author(s):

Cécile Pellet ◽

Christian Hauck

Keyword(s):

Soil Moisture ◽

Meteorological Data ◽

Monitoring Network ◽

High Elevation ◽

Time Domain Reflectometry ◽

Swiss Alps ◽

Freezing And Thawing ◽

Altitudinal Distribution ◽

Mountain Environments ◽

Stability Of Slopes

Abstract. Besides its important role in the energy and water balance at the soil-atmosphere interface, soil moisture can be a particular important factor in mountain environments since it influences the amount of freezing and thawing in the subsurface and can affect the stability of slopes. In permafrost areas, it is strongly linked to the ground ice content and by this modifies the characteristics and behaviour of periglacial landforms. In spite of its importance, the technical challenges and its strong spatial variability usually prevents soil moisture from being measured operationally at high and/or middle altitudes. This study describes the new Swiss soil moisture monitoring network SOMOMOUNT launched in 2013 consisting in six entirely automated soil moisture stations distributed along an altitudinal gradient between the Jura Mountains and the Swiss Alps, ranging from 1205 m to 3410 m elevation. In addition to the standard instrumentation comprising Frequency Domain Reflectometry (FDR) and Time Domain Reflectometry (TDR) sensors along vertical profiles, soil probes and meteorological data are available at each station. In this contribution we will present a detailed description of the SOMOMOUNT instrumentation and calibration procedures. Additionally, the data collected during the three first years of the project will be discussed in relation to their altitudinal distribution. Clear differences in soil moisture patterns are visible between sites with permanently and seasonally frozen as well as unfrozen ground conditions and can be related to several factors such as the subsurface composition (organic versus mineral), the elevation and the snow cover characteristics.

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Set-up and performance evaluation of a seismic rock fall observatory in the Alps

10.5194/egusphere-egu2020-14290 ◽

2020 ◽

Author(s):

Anne Schöpa ◽

Niels Hovius ◽

Jens Turowski

Keyword(s):

Data Storage ◽

Meteorological Data ◽

A Priori ◽

Monitoring Network ◽

Rock Fall ◽

Environmental Drivers ◽

Rock Falls ◽

Alpine Valley ◽

Mountain Environments ◽

Set Up

<p>Rock falls are important agents of erosion shaping the topography of bedrock slopes. Despite the considerable attention rock falls get when causing damage we still lack detailed information about the triggers, lag times, seasonal and elevation-dependent rock fall occurrence. This is due to the difficulty in observing rockfalls directly as the mobilisation of rock masses occurs rapidly, infrequently and distributed at a priori unknown locations. To identify seasonal and elevation-dependent rock fall activities and characteristics and their environmental drivers and triggers in an alpine setting, we have operated a monitoring network to detect and classify rock falls in the Reintal valley, German Alps, since 2014. The Reintal is an Alpine valley in the Wetterstein massif close to the Zugspitze, Germany&#8217;s highest mountain. The Reintal observatory produces nearly continuous datasets of seismic, meteorological and camera data. To our knowledge, these datasets are one of a few that permit a systematic study of rockfall patterns and their controls over a period of several years in an alpine setting.</p><p>In this contribution, we present the layout of the observatory and the instrumental network. Six seismometers record the motion of the ground; different types of seismic signals are shown and their sources discussed. This is done in combination with the meteorological data of the two weather stations in the valley and the images of the optical and infrared cameras of the observatory. We evaluate the performance, limitations and capabilities of the observatory. In addition, we discuss how we dealt with challenges such as power consumption of the instruments in the field, data storage and data loss. Our experience with the set-up and maintenance of the observatory can help guide the design and construction of other observatories in mountain environments.</p>

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Regional soil moisture monitoring network in the Raam catchment in the Netherlands

10.5194/essd-2017-50 ◽

2017 ◽

Cited By ~ 1

Author(s):

Harm-Jan F. Benninga ◽

Coleen D. U. Carranza ◽

Michiel Pezij ◽

Pim van Santen ◽

Martine J. van der Ploeg ◽

...

Keyword(s):

Soil Moisture ◽

The Netherlands ◽

Soil Temperature ◽

Meteorological Data ◽

Monitoring Network ◽

Water Shortage ◽

First Year ◽

Moisture Profile ◽

Groundwater Levels ◽

Extreme Precipitation Events

Abstract. We have established a soil moisture profile monitoring network in the 223 km2 Raam Catchment, a tributary of the Meuse River in the Netherlands. This catchment faces water shortage during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for calibration and validation of soil moisture products derived from earth observations or obtained by model simulations. Distributed over the Raam Catchment, we have equipped 14 agricultural fields and one natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m3 m−3. The first set of measurements has been retrieved for the period 5 April 2016–4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil texture, land cover, and geohydrological model) available for performing scientific research. The data is available at http://dx.doi.org/10.4121/uuid:2411bbb8-2161-4f31-985f-7b65b8448bc9.

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The Raam regional soil moisture monitoring network in the Netherlands

Earth System Science Data ◽

10.5194/essd-10-61-2018 ◽

2018 ◽

Vol 10 (1) ◽

pp. 61-79 ◽

Cited By ~ 10

Author(s):

Harm-Jan F. Benninga ◽

Coleen D. U. Carranza ◽

Michiel Pezij ◽

Pim van Santen ◽

Martine J. van der Ploeg ◽

...

Keyword(s):

Soil Moisture ◽

The Netherlands ◽

Soil Temperature ◽

Catchment Area ◽

Meteorological Data ◽

Monitoring Network ◽

Water Shortages ◽

Moisture Profile ◽

Groundwater Levels ◽

Extreme Precipitation Events

Abstract. We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km2), and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km2). Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m3 m−3. The first set of measurements has been retrieved for the period 5 April 2016–4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil physical characteristics, land cover and a geohydrological model) available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56.

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Variability in Soil Moisture by Natural and Artificial Snow: A Case Study in Mt. Balwang Area, Gangwon-do, South Korea

Frontiers in Earth Science ◽

10.3389/feart.2021.786356 ◽

2022 ◽

Vol 9 ◽

Author(s):

Yalalt Nyamgerel ◽

Hyejung Jung ◽

Dong-Chan Koh ◽

Kyung-Seok Ko ◽

Jeonghoon Lee

Keyword(s):

Soil Moisture ◽

Surface Water ◽

South Korea ◽

Snow Cover ◽

Isotopic Analysis ◽

High Elevation ◽

Hydrological Processes ◽

Soil Freezing ◽

Freezing And Thawing ◽

Natural Snow

Soil moisture is an important variable for understanding hydrological processes, and the year-round monitoring of soil moisture and temperature reflect the variations induced by snow cover and its melt. Herein, we monitored the soil moisture and temperature in high (two sites) and low (two sites) elevation regions with groundwater sampling near the Mt. Balwang area in Gangwon-do, South Korea from Sep 2020 to May 2021. This study aims to investigate the temporal and spatial variations in soil moisture and temperature due to snow (natural and artificial snow) and its melt. A ski resort has been operating in this area and has been producing artificial snow during winter periods; thus, the spring snowmelt comprises both natural and artificial snow. The effect of soil freezing and thawing, wind conditions, vegetation covers, the timing and intensity of snow cover and snowmelt were differed in the monitoring sites. The high elevation sites 1 and 2 exhibit the relatively longer and consistent snow cover than the low elevation sites. Particularly, site 2 show late (May 8) snow melting even this site is in south slope of the Mt. Balwang. The relatively steady and moist soil layers at sites 1, 2, and 3 during the warm period can be considered as influential points to groundwater recharge. Moreover, the differences between the mean δ18O (−9.89‰) of the artificial snow layers and other samples were low: in the order of surface water (0.04‰) >groundwater (−0.66 and −1.01‰) >natural snow (1.34 and −3.80‰). This indicates that the imprint of artificial snow derived from surface water and with decreasing amount of natural snow around the Mt. Balwang region, the results support the assumption that the potential influence of artificial snowmelt on groundwater quality. This study helps to understand the snow dynamic and its influence on the hydrological processes in this region by combining the hydro-chemical and isotopic analysis.

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IMPACTS OF MESIC RESTORATION STRUCTURES ON SOIL MOISTURE IN A HIGH-ELEVATION SAGEBRUSH STEPPE TRIBUTARY IN SOUTHWEST MONTANA, USA

10.1130/abs/2020rm-346693 ◽

2020 ◽

Author(s):

Lydia Landau ◽

◽

Rebekah Levine ◽

Eric Dyreson

Keyword(s):

Soil Moisture ◽

High Elevation ◽

Sagebrush Steppe

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Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack

Climatic Change ◽

10.1007/s10584-021-03027-x ◽

2021 ◽

Vol 165 (3-4) ◽

Author(s):

Maria Vorkauf ◽

Christoph Marty ◽

Ansgar Kahmen ◽

Erika Hiltbrunner

Keyword(s):

Snow Depth ◽

Growing Season ◽

High Elevation ◽

Alpine Plants ◽

Swiss Alps ◽

Climate Change Scenarios ◽

Freezing Damage ◽

Strong Shift ◽

Air Temperatures ◽

Early Snowmelt

AbstractThe start of the growing season for alpine plants is primarily determined by the date of snowmelt. We analysed time series of snow depth at 23 manually operated and 15 automatic (IMIS) stations between 1055 and 2555 m asl in the Swiss Central Alps. Between 1958 and 2019, snowmelt dates occurred 2.8 ± 1.3 days earlier in the year per decade, with a strong shift towards earlier snowmelt dates during the late 1980s and early 1990s, but non-significant trends thereafter. Snowmelt dates at high-elevation automatic stations strongly correlated with snowmelt dates at lower-elevation manual stations. At all elevations, snowmelt dates strongly depended on spring air temperatures. More specifically, 44% of the variance in snowmelt dates was explained by the first day when a three-week running mean of daily air temperatures passed a 5 °C threshold. The mean winter snow depth accounted for 30% of the variance. We adopted the effects of air temperature and snowpack height to Swiss climate change scenarios to explore likely snowmelt trends throughout the twenty-first century. Under a high-emission scenario (RCP8.5), we simulated snowmelt dates to advance by 6 days per decade by the end of the century. By then, snowmelt dates could occur one month earlier than during the reference periods (1990–2019 and 2000–2019). Such early snowmelt may extend the alpine growing season by one third of its current duration while exposing alpine plants to shorter daylengths and adding a higher risk of freezing damage.

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Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽

10.3390/w13010037 ◽

2020 ◽

Vol 13 (1) ◽

pp. 37

Author(s):

Tomás de Figueiredo ◽

Ana Caroline Royer ◽

Felícia Fonseca ◽

Fabiana Costa de Araújo Schütz ◽

Zulimar Hernández

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Regression Models ◽

Meteorological Data ◽

Water Storage ◽

Model Performance ◽

Soil Water Balance ◽

Soil Water Storage ◽

The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

Sustainability ◽

10.3390/su13158263 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8263

Author(s):

Marius Bodor

Keyword(s):

Particulate Matter ◽

Meteorological Data ◽

Monitoring Network ◽

Pollution Monitoring ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Indoor Location ◽

Particle Matter ◽

Indoor Particulate Matter

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

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