Recommendations for gap-filling eddy covariance latent heat flux measurements using marginal distribution sampling

Foltýnová, Lenka; Fischer, Milan; McGloin, Ryan

doi:10.1007/s00704-019-02975-w

Cited by 19 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the strong seasonal and diurnal variation of ET, linear interpolation is not recommended. A standard procedure is to use the marginal distribution sampling (MDS) gap-filling algorithm, which considers meteorological variables to account for the daily and annual seasonality (Falge et al, 2001;Foltýnová et al, 2020;Reichstein et al, 2005;Wutzler et al, 2018). The monthly and yearly values of ET from MDS gapfilling will later be compared with the modelled ET predictions.…”

Section: Eddy Covariance Flux Towersmentioning

confidence: 99%

“…These instruments can provide quite different estimates as soil evaporation, transpiration and interception loss are very distinctive processes which are challenging to separate, particularly when measured by micrometeorological approaches (Miralles et al, 2020).ET is mainly driven by atmospheric conditions such as sunlight intensity (i.e. incoming radiation), air temperature and relative humidity (Foltýnová et al, 2020). In contrast, the volume of ET over the year depends greatly on the land surface and the water availability in the soil (Dwarakish et al, 2015;Wang et al, 2020;Zheng et al, 2020).…”

mentioning

confidence: 99%

“…In climatology, ET is often measured by the eddy covariance method, which is based on the turbulence flux and energy balance (Liang and Wang, 2020). This method is affected by atmospheric stability, wind profile and surface roughness in the surroundings of the flux tower (Foltýnová et al, 2020;Schmid and Oke, 1990;Ward and Grimmond, 2017). The ET observations are continually collected over regular time intervals but represented by an irregular area based on footprints which change in shape, size and orientation according to atmospheric conditions (Kljun et al, 2002;Kotthaus and Grimmond, 2014).Traditional point-based techniques to measure ET are insufficient in a heterogeneous urban environment (Nouri et al, 2013).ET observations collected by lysimeters are affected by percolation, runoff, soil moisture and the volume of the ground table water (Nouri et al, 2019).…”

mentioning

confidence: 99%

“…ET is mainly driven by atmospheric conditions such as sunlight intensity (i.e. incoming radiation), air temperature and relative humidity (Foltýnová et al, 2020). In contrast, the volume of ET over the year depends greatly on the land surface and the water availability in the soil (Dwarakish et al, 2015;Wang et al, 2020;Zheng et al, 2020).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Rocha¹,

Vulova²,

Tol

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Evapotranspiration (ET) is a fundamental variable to assess water balance and urban heat island effect. ET is deeply dependent on the land cover as it derives mainly from the processes of soil evaporation and plant transpiration. The majority of well-known process-based models based on the Penman-Monteith equation focus on the atmospheric interfaces (e.g. radiation, temperature and humidity), lacking explicit input parameters to describe the land surface. The model Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) accounts for a broad range of surface-atmosphere interactions to predict ET. However, like most modelling approaches, SCOPE assumes a homogeneous vegetated landscape to estimate ET. Urban environments are highly fragmented, exhibiting a blend of pervious and impervious anthropogenic surfaces. Whereas, high-resolution remote sensing (RS) and detailed GIS information to characterise land surfaces is usually available for major cities. Data describing land surface properties were used in this study to develop a method to correct bias in ET predictions caused by the assumption of homogeneous vegetation by process-based models. Two urban sites equipped with eddy flux towers presenting different levels of vegetation fraction and imperviousness located in Berlin, Germany, were used as study cases. The correction factor for urban environments has increased model accuracy significantly, reducing the relative bias in ET predictions from 0.74 to −0.001 and 2.20 to −0.13 for the two sites, respectively, considering the SCOPE model using RS data. Model errors (i.e. RMSE) were also considerably reduced in both sites, from 0.061 to 0.026 and 0.100 to 0.021, while the coefficient of determination (R2) remained similar after the correction, 0.82 and 0.47, respectively. This study presents a novel method to predict hourly urban ET using freely available RS and meteorological data, independently from the flux tower measurements. The presented method can support actions to mitigate climate change in urban areas, where most the world population lives.

show abstract

Section: Eddy Covariance Flux Towersmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Rocha¹,

Vulova²,

Tol

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The advantage of inferential modeling is that long gaps in flux time series can be filled. This is not possible with MDV or other recently published gap-filling methods (e.g., Falge et al, 2001;Reichstein et al, 2005;Moffat et al, 2007;Wutzler et al, 2018;Foltýnová et al, 2020;Kim et al, 2020) because the latter are optimized for inert gases. Statistical methods like MDV assume a periodic variability of fluxes.…”

Section: Uncertainties Of Flux Measurements and Gap-filling Approachesmentioning

confidence: 99%

Forest-atmosphere exchange of reactive nitrogen in a low polluted area – temporal dynamics and annual budgets

Wintjen

Schrader

Schaap

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Accurate modeling of nitrogen deposition is essential for identifying exceedances of critical loads and designing effective mitigation strategies. However, there are still uncertainties in modern deposition routines due to a limited availability of long-term flux measurements of reactive nitrogen compounds for model development and validation. In this study, we investigate the performance of dry deposition inferential models with regard to annual budgets and the exchange patterns of total reactive nitrogen (ΣNr) at a low-polluted mixed forest located in the Bavarian Forest National Park (NPBW), Germany. Flux measurements of ΣNr were carried out with a Total Reactive Atmospheric Nitrogen Converter (TRANC) coupled to a chemiluminescence dectector (CLD) for 2.5 years. Average ΣNr concentration was approximately 5.2 ppb. Denuder measurements with DELTA samplers and chemiluminescence measurements of nitrogen oxides (NOx) have shown that NOx has the highest contribution to ΣNr (~52%), followed by ammonia (NH3) (~ 22 %), ammonium (NH4+) (~ 14 %), nitrate NO3− (~ 7 %), and nitric acid (HNO3) (~ 6 %). We observed mostly deposition fluxes at the measurement site with median fluxes ranging from −15 ng N m−2 s−1 to −5 ng N m−2 s−1 (negative fluxes indicate deposition). In general, highest deposition was recorded from May to September. ΣNr deposition was enhanced by higher temperatures, lower relative humidity, high ΣNr concentration, and dry leaf surfaces. Our results suggest that dry conditions seem to favour nitrogen dry deposition at natural ecosystems. For determining annual dry deposition budgets we used the bidirectional inferential scheme DEPAC (DEPosition of Acidifying Compounds) with locally measured input parameters, called DEPAC-1D, as gap-filling strategy for TRANC measurements. In a second approach, the mean-diurnal-variation method (MDV) was applied to gaps of up to five days whereas DEPAC-1D was used for remaining gaps. We compared them to results from the chemical transport model LOTOS-EUROS (LOng Term OzoneSimulation – EURopean Operational Smog) v2.0 and from the canopy budget technique conducted at the measurement site. After 2.5 years, dry deposition based on TRANC measurements resulted in (11.1 ± 3.4) kg N ha−1 with DEPAC-1D as gap-filling method and (10.9 ± 3.8) kg N ha−1 with MDV and DEPAC-1D as gap-filling methods. Both values are close to dry deposition by DEPAC-1D (13.6 kg N ha−1) considering the uncertainties of measured fluxes and possible uncertainty sources of DEPAC-1D. The difference of DEPAC-1D to TRANC can be related to parameterizations of reactive gases or the missing exchange path with soil. 16.8 kg N ha−1 deposition were calculated by LOTOS-EUROS for considering land-use class weighting. We further showed that predicted NH3 concentrations, an input parameter of LOTOS-EUROS, were the main reason for the discrepancyin dry deposition budgets between the different methods. On average, annual TRANC dry deposition was 4.5 kg N ha−1 a−1 for both gap-filling approaches, DEPAC-1D showed 5.3 kg N ha−1 a−1, and LOTOS-EUROS modeled 5.2 kg N ha−1 a−1 to 6.9 kg N ha−1 a−1 depending on the weighting of land-use classes within the site's grid cell. 7.5 kg N ha−1 a−1 was estimated with the canopy budget technique for the period from 2016 to 2018 as upper estimate and 4.6 kg N ha−1 a−1 as lower estimate. Our findings provide a better understanding of exchange dynamics occurring at low-polluted, natural ecosystems and show opportunities for further development of deposition models.

show abstract

Evapotranspiration over a processing cassava field: a comparative analysis of micrometeorological methods and remote sensing

da Silva,

de Oliveira,

Filho

2024

Theor Appl Climatol

View full text Add to dashboard Cite

Recommendations for gap-filling eddy covariance latent heat flux measurements using marginal distribution sampling

Cited by 19 publications

References 28 publications

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Forest-atmosphere exchange of reactive nitrogen in a low polluted area – temporal dynamics and annual budgets

Evapotranspiration over a processing cassava field: a comparative analysis of micrometeorological methods and remote sensing

Contact Info

Product

Resources

About