On the performance of microlysimeters to measure non-rainfall water input in a hyper-arid environment with focus on fog contribution

Feigenwinter, Christian; Franceschi, Joel; Larsen, Jarl Are; Spirig, Robert; Vogt, Roland

doi:10.1016/j.jaridenv.2020.104260

Cited by 18 publications

(24 citation statements)

References 49 publications

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“…The accuracy of our ML system was four orders of magnitude better than reported for many other studies (see Table 2). Feigenwinter et al (2020) could achieve on average (depending on calibration date) the same accuracy, although with a lower depth of the ML pot (6.5 cm) and a lower weighing capacity (7kg). The high accuracy of our ML system was achieved by a combination of factors, such as using a state-of-the-art load cell in combination with continuous high frequency data filtering as well as ancillary data.…”

Section: Accuracy Of the ML Systemmentioning

confidence: 92%

“…The load cell capacity of 20 kg in our ML system is relatively large compared to other ML studies. NRW input studies with ML had a load cell capacity in the range from 0.3 kg (Brown et al, 2008), 1.5 kg (Kaseke et al, 2012), 3 kg (Uclés et al, 2013, 6 kg (Maphangwa et al, 2012;Matimati et al, 2013), up to 7 kg (Feigenwinter et al, 2020). Precision ranged from ± 0.1 g (± 0.002 mm) to ± 1.12 g (± 0.023 mm), depending on calibration date…”

Section: Accuracy Of the ML Systemmentioning

confidence: 99%

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Technical note: High accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

Riedl

Buchmann

et al. 2021

Preprint

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Abstract. Non-rainfall water (NRW), defined here as dew, hoar frost, fog, rime and water vapor adsorption, might be a relevant water source for ecosystems, especially during summer drought periods. These water inputs are often not considered in ecohydrological studies, because water amounts of NRW events are rather small and therefore difficult to measure. Here we present a novel micro-lysimeter (ML) system and its application which allows to quantify very small water inputs from NRW with an unprecedented high accuracy of ±0.25 g, which corresponds to ±0.005 mm water input. This is possible with an improved ML design paired with individual ML calibrations in combination with high-frequency measurements at 3.3 Hz and an efficient low-pass filtering to reduce noise level. With a set of ancillary sensors, the ML system furthermore allows differentiating between different types of NRW inputs: dew, hoar frost, fog, rime and the combinations among these, but also additional events when condensation on leaves is less probable, such as water vapor adsorption events. In addition, our ML system design allows to minimize deviations from natural conditions in terms of canopy and soil temperatures, plant growth and soil moisture. This is found to be a crucial aspect for obtaining realistic NRW measurements in short-statured grasslands. Our ML system has proven to be useful for high-accuracy, long-term measurements of NRW on short-statured vegetation like grasslands. Measurements with the ML system at a field site in Switzerland showed that NRW input occurred frequently with 127 events over 12 months, with a total NRW input of 15.9 mm. High average monthly NRW inputs were measured during summer months, suggesting a high ecohydrological relevance of NRW inputs for temperate grasslands.

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Section: Accuracy Of the ML Systemmentioning

confidence: 92%

Section: Accuracy Of the ML Systemmentioning

confidence: 99%

Technical note: High accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

Riedl

Buchmann

et al. 2021

Preprint

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“…Additionally, three microlysimeters were deployed (Fig. 2 J) to measure potential deposition of fog water during nocturnal fog events and to estimate evaporation (Feigenwinter et al 2020).…”

Section: E Sub-surface Temperature and Heat Transfer Measurementsmentioning

confidence: 99%

The Namib Turbulence Experiment: Investigating Surface–Atmosphere Heat Transfer in Three Dimensions

Hilland

Bernhofer

Bohmann

et al. 2022

Bulletin of the American Meteorological Society

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The Namib Turbulence EXperiment (NamTEX) was a multi-national micrometeorological campaign conducted in the Central Namib Desert to investigate three-dimensional surface layer turbulence and the spatio-temporal patterns of heat transfer between the sub-surface, surface, and atmosphere. The Namib provides an ideal location for fundamental research that revisits some key assumptions in micrometeorology that are implicitly included in the parameterizations describing energy exchange in weather forecasting and climate models: Homogenous flat surfaces, no vegetation, little moisture, and cloud-free skies create a strong and consistent diurnal forcing, resulting in a wide range of atmospheric stabilities. A novel combination of instruments was used to simultaneously measure variables and processes relevant to heat transfer: A three km fibre-optic distributed temperature sensor (DTS) was suspended in a pseudo-three-dimensional array within a 300 m x 300 m domain to provide vertical cross-sections of air temperature fluctuations. Aerial and ground-based thermal imagers recorded high resolution surface temperature fluctuations within the domain and revealed the spatial thermal imprint of atmospheric structures responsible for heat exchange. High-resolution soil temperature and moisture profiles together with heat flux plates provided information on near-surface soil dynamics. Turbulent heat exchange was measured with a vertical array of five eddy-covariance point measurements on a 21-m mast, as well as by co-located small- and large-aperture scintillometers. This contribution first details the scientific goals and experimental set-up of the NamTEX campaign. Then using a typical day, we demonstrate i) the coupling of surface layer, surface, and soil temperatures using high-frequency temperature measurements, ii) differences in spatial and temporal standard deviations of the horizontal temperature field using spatially distributed measurements, and iii) horizontal anisotropy of the turbulent temperature field.

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“…NRW is brought to the rhizosphere by drip-off from leaves and stems (Dawson, 1998) or by dew formation and/or fog droplet interception and impaction on soils (Agam and Berliner, 2006;Kaseke et al, 2012;Uclés et al, 2013). Moreover, NRW can also reduce water loss (1) by suppressing transpiration (Aparecido et al, 2016;Gerlein-Safdi et al, 2018;Ishibashi and Terashima, 1995;Waggoner et al, 1969), induced by clogged stomata (Gerlein-Safdi et al, 2018;Vesala et al, 2017), (2) by reducing the vapour pressure deficit (Ritter et al, 2009) in the boundary layer between leaves and the atmosphere, and (3) by decreasing canopy temperatures because of evaporative cooling during re-evaporation of NRW inputs (Thornthwaite, 1948). The energy from incoming solar radiation is partially used for the phase transition from liquid water to water vapour, which thereby alleviates potential heat stress of the plants.…”

Section: Introductionmentioning

confidence: 99%

Technical note: High-accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

Riedl

Eugster

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Non-rainfall water (NRW), defined here as dew, hoar frost, fog, rime, and water vapour adsorption, might be a relevant water source for ecosystems, especially during summer drought periods. These water inputs are often not considered in ecohydrological studies, because water amounts of NRW events are rather small and therefore difficult to measure. Here we present a novel micro-lysimeter (ML) system and its application which allows us to quantify very small water inputs from NRW during rain-free periods with an unprecedented high accuracy of ±0.25 g, which corresponds to ±0.005 mm water input. This is possible with an improved ML design paired with individual ML calibrations in combination with high-frequency measurements at 3.3 Hz and an efficient low-pass filtering to reduce noise level. With a set of ancillary sensors, the ML system furthermore allows differentiation between different types of NRW inputs, i.e. dew, hoar frost, fog, rime, and the combinations among these, but also additional events when condensation on leaves is less probable, such as water vapour adsorption events. In addition, our ML system design allows one to minimize deviations from natural conditions in terms of canopy and soil temperatures, plant growth, and soil moisture. This is found to be a crucial aspect for obtaining realistic NRW measurements in short-statured grasslands. Soil temperatures were higher in the ML compared to the control, and thus further studies should focus on improving the thermal soil regime of ML. Our ML system has proven to be useful for high-accuracy, long-term measurements of NRW on short-statured vegetation-like grasslands. Measurements with the ML system at a field site in Switzerland showed that NRW input occurred frequently, with 127 events over 12 months with a total NRW input of 15.9 mm. Drainage-water flow of the ML was not measured, and therefore the NRW inputs might be conservative estimates. High average monthly NRW inputs were measured during summer months, suggesting a high ecohydrological relevance of NRW inputs for temperate grasslands.

show abstract

On the performance of microlysimeters to measure non-rainfall water input in a hyper-arid environment with focus on fog contribution

Cited by 18 publications

References 49 publications

Technical note: High accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

Technical note: High accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

The Namib Turbulence Experiment: Investigating Surface–Atmosphere Heat Transfer in Three Dimensions

Technical note: High-accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

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