# Evapotranspiration of an Abandoned Grassland in the Italian Alps: Influence of Local Topography, Intra- and Inter-Annual Variability and Environmental Drivers

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Site Description

#### 2.2. Instrumentation

#### 2.3. Data Processing, Quality Control and Analysis

#### 2.3.1. Eddy Covariance and Radiation Fluxes

#### 2.3.2. Soil Variables and Surface Energy Balance

#### 2.3.3. Eddy Fluxes Gapfilling and Flux Footprint Estimate

#### 2.3.4. Statistical Methods

## 3. Results

#### 3.1. Topography Influence on Actual Evapotranspiration and Its Inter-Annual Variability

#### 3.1.1. Wind Regime and Latent Heat Flux Sources

#### 3.1.2. Morning Latent Heat Flux Inflexion and Mean Diurnal Cycles of Fluxes

#### 3.2. Micrometeorological Intra- and Inter-Annual Variability

#### 3.2.1. Meteo-Climatic Variability at the Experimental Site

#### 3.2.2. Average Daily and Cumulative Actual Evapotranspiration in the Growing Seasons

#### 3.2.3. Bimodality of Environmental Variables

#### 3.2.4. Evaporative Fraction, Soil Water Content and Evapotranspiration Regimes

#### 3.2.5. Fifteen-Days Analysis of Micrometeorological Variables

#### 3.3. Evapotranspiration Environmental Drivers

#### 3.3.1. Actual Evapotranspiration Linear Mixed Effects Model and Multivariate Regressions with Environmental Drivers

#### 3.3.2. Actual Evapotranspiration Univariate Regressions with Environmental Drivers

#### 3.3.3. Actual Evapotranspiration Relationship with Precipitation and Soil Water Content

## 4. Discussion

#### 4.1. Topography Influence on Actual Evapotranspiration and Its Inter-Annual Variability

#### 4.2. Micrometeorological Intra-and Inter-Annual Variability

#### 4.2.1. Meteo-Climatic Variability at the Experimental Site

#### 4.2.2. Average Daily and Cumulative Actual Evapotranspiration in the Growing Seasons

#### 4.2.3. Inter-Annual Variability of Actual Evapotranspiration and Precipitation: A Focus

#### 4.2.4. Bimodality of Latent Heat Flux

#### 4.2.5. Evaporative Fraction, Soil Water Content and Actual Evapotranspiration Regimes

#### 4.2.6. Fifteen-Days Micrometeorological Analysis

#### 4.3. Actual Evapotranspiration Environmental Drivers

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Symbols and Abbreviations

Symbol | Variable | Unit |

$LE$ | Latent heat flux | W m${}^{-2}$ |

H | Sensible heat flux | W m${}^{-2}$ |

${R}_{n}$ | Net radiation | W m${}^{-2}$ |

${R}_{g}$ | Global radiation | W m${}^{-2}$ |

G | Ground heat flux | W m${}^{-2}$ |

${G}_{0}$ | Ground heat flux at the land surface | W m${}^{-2}$ |

${S}_{soil}$ | Heat storage flux above the soil heat flux plates | W m${}^{-2}$ |

${S}_{canopy}$ | Heat storage flux within the canopy | W m${}^{-2}$ |

S | Overall (soil + canopy heat storage flux) | W m${}^{-2}$ |

${T}_{air}$ | Air temperature | °C |

U | Wind speed | m s${}^{-1}$ |

RH | Relative humidity | (-) |

VPD | Vapour pressure deficit | Pa |

$\theta $ | Soil water content (at 10–20 cm depth if not specified, or at 40 cm if specified) | m${}^{3}$ m${}^{-3}$ |

ETa | Actual evapotranspiration | mm |

ETo | Potential evapotranspiration | mm |

EF | Evaporative fraction | (-) |

P | Precipitation | mm |

Abbreviation | Meaning | |

EC | Eddy covariance | |

AIC | Akaike Information Criterion | |

AICc | Corrected Akaike Information Criterion | |

MJJAS | May-September period | |

JJAS | June-September period (growing season) |

## Appendix A

**Figure A1.**Number of grassland stations in Europe. The blue bar indicates the altitude range in which the Cogne site is located.

Site | Altitude | Terrain | Land Use | EC | Reference |
---|---|---|---|---|---|

AT-Sta | 1970 | strong slope | abandoned grassland | no | [32] |

AT-Stm | 1770 | strong slope | managed meadow | yes | [11] |

AT-Stp | 1850 | strong slope | managed meadow | no | [32] |

CH-Aws | 1978 | flat | grassland | yes | [103] |

CH-Dsc | 1590 | flat | grassland | yes | [104] |

CH-Frk | 2100 | strong slope | grassland | yes | [105] |

ES-Cst | 1900 | gentle slope | grassland | yes | [106] |

ES-VdA | 1770 | gentle slope | managed meadow | yes | [107] |

FR-Clt | 2000 | signif. slope | grassland | no | [108] |

IT-Mal | 2000 | gentle slope | grassland | yes | [109] |

IT-Mbo | 1550 | almost flat | grassland | yes | [110] |

IT-Mtm | 1500 | strong slope | managed meadow | yes | [111] |

IT-Mtp | 1500 | strong slope | grassland | yes | [111] |

IT-Tor | 2150 | gentle slope | abandoned grassland | yes | [33] |

**Table A2.**Extremes of physical plausible data for the Cogne site for wind speed anemometric components (u, v, w), sonic temperature (${T}_{s}$), CO${}_{2}$ and H${}_{2}$O concentration values.

u | v | w | ${\mathit{T}}_{\mathit{s}}$ | [CO${}_{2}$] | [H${}_{2}$O] | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

(m s${}^{-1}$) | (m s${}^{-1}$) | (m s${}^{-1}$) | (°C) | ($\mathsf{\mu}$mol m${}^{-3}$) | (mmol m${}^{-3}$) | ||||||

MIN | MAX | MIN | MAX | MIN | MAX | MIN | MAX | MIN | MAX | MIN | MAX |

−30 | +30 | −30 | +30 | −5 | +5 | −40 | +50 | 7 | 30 | 0 | 1800 |

**Figure A3.**Mean diurnal cycles for quality control on the data set. Green: good quality data. Red: bad quality data. (

**a**) Sensible heat flux. (

**b**) Latent heat flux.

**Figure A4.**Daytime wind roses at the eddy covariance site (

**left**) and at the Regional Authority (ARPA-VdA) site (

**right**). Figure done with Openair R package [112].

**Figure A5.**Mean diurnal cycles of daytime sensible heat flux (H), vapour pressure deficit (VPD), relative humidity (RH) and wind direction for the four growing seasons. Half-hourly data. (

**a**) 2014. (

**b**) 2015. (

**c**) 2016. (

**d**) 2017.

**Table A3.**Results of the multivariate regression of actual evapotranspiration against the considered drivers. p-Values for Student t (threshold set to 0.05) and Akaike information criterion corrected for small sample sizes (AICc) values for each growing season. Illustrated AICc values are prior to AICc stepwise minimisation.

Year | Driver | p-Value | AICc |
---|---|---|---|

2014 | ${R}_{n}$ | ~0 | 306.9 |

VPD | 0.007 | 295.1 | |

U | ~0 | 305.2 | |

${G}_{0}$ | 0.16 | 289.5 | |

${T}_{air}$ | 0.13 | 289.8 | |

2015 | ${R}_{n}$ | ~0 | 164.9 |

VPD | ~0 | 185.1 | |

U | ~0 | 153.4 | |

${G}_{0}$ | 0.16 | 135.0 | |

${T}_{air}$ | 0.003 | 142.6 | |

2016 | ${R}_{n}$ | ~0 | 202.3 |

VPD | ~0 | 134.8 | |

U | ~0 | 175.0 | |

${G}_{0}$ | ~0 | 124.1 | |

${T}_{air}$ | 0.26 | 108.9 | |

2017 | ${R}_{n}$ | ~0 | 126.5 |

VPD | 0.03 | 66.2 | |

U | ~0 | 86.1 | |

${G}_{0}$ | ~0 | 85.3 | |

${T}_{air}$ | 0.001 | 72.9 |

**Figure A6.**Frequency density distributions (in every growing season) of three micrometeorological variables. (

**a**) Wind speed (U). (

**b**) Vapour pressure deficit. (

**c**) Air temperature. Dashed lines represent the mean values for each variable in each growing season. Based on 2015 data as an example, light red histograms represent the density distribution between 05:00 and 7:00 and between 17:00 and 20:00. The grey histograms refer to the hours between 08:00 and 16:00.

**Figure A7.**(

**a**) Actual evapotranspiration versus average shallow soil water content (10–20 cm of depth) considering 15-days periods for each growing season. (

**b**) Actual evapotranspiration versus average shallow soil water content (10–20 cm of depth) considering 52-days periods for each growing season. Ellipses highlight the different regions of the plane in which the data lay in different years. The cumulative actual evapotranspiration was normalised by the number of days of each considered time period.

**Figure A8.**(

**a**) Actual evapotranspiration (ETa) versus cumulative precipitation (cumulative P) considering 15, 36, 35 and 53 days periods. (

**b**) Actual evapotranspiration (ETa) versus average shallow soil water content ($\theta $, 10–20 cm of depth) considering 15, 36, 35 and 52 days periods. None of the regressions is significant.

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**Figure 1.**The Cogne experimental site with the flux footprint labelled in the second panel with a satellite image of the site area (Google Earth, © Google 2021). The contour lines of the flux footprint enclose the area which contributes 80% of the total flux.

**Figure 2.**Mean latent heat flux ($LE$) and wind frequency contour maps as a function of wind direction and hours of the day (left coloured panels) and wind speed mean diurnal cycle (right white panels) The left coloured panels show the wind direction contour lines (for both daytime and nighttime) and the latent heat flux colour map for the four average June-to-September periods, between 6:00 and 20:00, when most of the evapotranspiration occurs. The right white panels show the mean wind speed along the day, together with the standard deviation of values for each half-hour. Asterisks indicate outliers (i.e. values outside 1.5 times the interquartile range or IQR, above the upper quartile or below the lower quartile). Grey areas indicate no $LE$ data (nighttime was excluded). Plot made using Metvurst R package [81]. (

**a**) 2014. (

**b**) 2015. (

**c**) 2016. (

**d**) 2017.

**Figure 3.**Growing season mean diurnal cycles at half hourly temporal resolution for latent heat flux ($LE$), net radiation (${R}_{n}$), wind direction and air temperature (${T}_{air}$). (

**a**) 2014. (

**b**) 2015. (

**c**) 2016. (

**d**) 2017. Dashed lines refer to $LE$ ± 1$\sigma $. The morning inflexion is visible in all the growing seasons for ${T}_{air}$ and $LE$.

**Figure 4.**Diurnal cycles at half hourly temporal resolution for latent heat flux ($LE$), global radiation (${R}_{g}$), net radiation (${R}_{n}$), air temperature (${T}_{air}$), wind direction and relative humidity (RH). (

**a**) 4 July 2017. (

**b**) Mean diurnal cycles of the same variables, for the period 4–7 July 2017. The morning inflexion is visible in all the panels.

**Figure 5.**June-to-September (JJAS) period 1995–2019 (data from ARPA-VdA station). (

**a**) Average air temperature. (

**b**) Cumulative precipitation. Red squares represent the 2014–2017 data mean air temperature and precipitation. Black circles represent the same variables but in the other years.

**Figure 6.**(

**a**) Box plots of daily actual evapotranspiration for each growing season. Whiskers indicate the range within ±1.5 IQR (Interquartile Range), and the horizontal line is the median value. The red dots are mean values. (

**b**) Cumulative precipitation and actual evapotranspiration for the growing seasons in each year.

**Figure 7.**Frequency density distributions (in every growing season, JJAS) for: (

**a**) Latent heat flux ($LE$). (

**b**) Net radiation (${R}_{n}$). (

**c**) Shallow volumetric soil water content ($\theta $, at 10-20 cm of soil depth). (

**d**) Surface ground heat flux (${G}_{0}$). Dashed lines represent the mean values for each variable in each growing season. Based on 2015 data as an example, light red histograms represent the density distribution of $LE$ and ${R}_{n}$ between 05:00 and 7:00 and between 17:00 and 20:00. The grey histograms refer to the hours between 08:00 and 16:00. For soil water content, the light red histogram refers to the dry period (DOY 185–223). The grey and green histograms are instead related to the wet periods (DOY 164–184 and 224–253, respectively). The dotted lines represent the soil water content at wilting point (black) and at field capacity (brown).

**Figure 8.**(

**a**) Daily averages (considering the hours 8:00–15:00) of Evaporative Fraction (EF = $LE/H+LE$) versus daily shallow (10–20 cm of depth) volumetric soil water content $\theta $. (

**b**) EF value at local 12:00 versus daily soil water content. Continuous lines in Panels (a,b) represent the wilting point (black) and the field capacity (brown). Regression curves of Panels (a,b) were computed to smooth the EF using the loess filter provided in the “stats” R-package [72]. With the approach described by [83]).

**Figure 9.**Daily precipitation bars, potential and actual evapotranspiration (ETo and ETa, respectively) and measured soil water content ($\theta $) expressed in millimetres at 10 or 20 cm and at 40 cm of depth for the four growing seasons for a better comparison. The daily values of the growing seasons are shown for (

**a**) 2014. (

**b**) 2015. (

**c**) 2016. (

**d**) 2017.

**Figure 10.**Micrometeorological variables on periods of 15 days. (

**a**) Average air temperature (${T}_{air}$). (

**b**) Average vapour pressure deficit (VPD). (

**c**) Average precipitation (rain) per day (considering all days for each period). (

**d**) Number of dry days. (

**e**) Average evaporative fraction (EF). (

**f**) Cumulative actual evapotranspiration (ETa).

**Figure 11.**VPD (circles) and air temperature (triangles) during the daytime hours between 7:00 and 19:00 from 2014 to 2017 growing seasons.

**Figure 12.**(

**a**) Actual evapotranspiration (ETa) versus cumulative precipitation (cumulative P) considering 15-days periods for each growing season. (

**b**) Actual evapotranspiration (ETa) versus cumulative precipitation (cumulative P) considering 52-days periods for each growing season. Ellipses highlight the different regions of the plane in which the data lay in different years. The cumulative actual evapotranspiration was normalised by the number of days of each considered time period.

**Table 1.**Energy balance closure slopes after the linear regression. All regressions were significant (p < 0.05). The intercept was not forced through zero.

Year | Slope (-) | R^{2} |
---|---|---|

2014 | 0.70 (0.68–0.73) | 0.63 (n = 1931) |

2015 | 0.70 (0.68–0.71) | 0.73 (n = 1761) |

2016 | 0.60 (0.59–0.62) | 0.82 (n = 2422) |

2017 | 0.60 (0.59–0.61) | 0.84 (n = 1804) |

**Table 2.**Average and maximum dry spell for May-to-September (MJJAS) periods and average relative humidity (RH) for June-to-September (JJAS) periods. Cumulative precipitation for May, June-to-September periods and for the hydrological years. Average air temperature (${T}_{air}$) for the JJAS period. The range reported for the average dry spell and air temperature is one standard deviation.

Year | Dry Spell (MJJAS) | RH (JJAS) | Precipitation (May) | Precipitation (JJAS) | Precipitation (Year) | T_{air} (JJAS) | |
---|---|---|---|---|---|---|---|

AVG | MAX | AVG | SUM | SUM | SUM | AVG | |

(Days) | (Days) | (%) | (mm) | (mm) | (mm) | (°C) | |

2014 | 4.2 ± 3.4 | 14 | 67.1 | 69.4 | 263.0 | 696.0 | 12.0 ± 4.4 |

2015 | 6.6 ± 6.7 | 28 | 64.0 | 66.2 | 317.6 | 757.0 | 13.3 ± 5.6 |

2016 | 5.5 ± 4.2 | 15 | 58.6 | 111.8 | 179.0 | 640.0 | 13.4 ± 5.2 |

2017 | 6.1 ± 5.0 | 20 | 57.4 | 53.8 | 182.6 | 624.0 | 13.2 ± 5.7 |

2014 | 2015 | 2016 | 2017 |
---|---|---|---|

58.0% | 52.1% | 41.5% | 43.3% |

**Table 4.**Results of the multivariate regression of actual evapotranspiration against the linear combination of the considered drivers. p-values for Student t (threshold set to 0.05) and Akaike information criterion (AIC) values. Illustrated AIC values are prior to AIC stepwise minimisation. All the growing seasons are considered without inter-annual distinctions. ${R}^{2}$ = 0.56 (adjusted value, considering all drivers).

Driver | p-Value | AIC |
---|---|---|

Rn | ~0 | −144.3 |

U | ~0 | −190.2 |

VPD | 0.02 | −203.5 |

${G}_{0}$ | 0.45 | −209.1 |

${T}_{air}$ | 0.15 | −209.2 |

**Table 5.**${R}^{2}$ of the multivariate regression of actual evapotranspiration against the linear combination of the considered drivers using all growing seasons without inter-annual distinctions. (All regressions are significant at 95% confidence level).

2014 | 2015 | 2016 | 2017 | |
---|---|---|---|---|

${\mathit{R}}^{\mathbf{2}}$ | 0.67 | 0.87 | 0.86 | 0.83 |

**Table 6.**${R}^{2}$ of the univariate regressions of actual evapotranspiration against each considered driver using all the growing seasons without inter-annual distinctions. (All regressions are significant at 95% confidence level).

Driver | p-Value | ${\mathit{R}}^{2}$ |
---|---|---|

${R}_{n}$ | ~0 | 0.52 |

VPD | ~0 | 0.43 |

U | ~0 | 0.13 |

${G}_{0}$ | ~0 | 0.44 |

${T}_{air}$ | ~0 | 0.30 |

**Table 7.**${R}^{2}$ of the univariate regressions of actual evapotranspiration against each considered driver in each growing season. (All regressions are significant at 95% confidence level).

Year | ${\mathit{R}}_{\mathit{n}}$ | VPD | U | ${\mathit{G}}_{0}$ | ${\mathit{T}}_{\mathbf{air}}$ |
---|---|---|---|---|---|

2014 | 0.54 | 0.58 | 0.12 | 0.51 | 0.38 |

2015 | 0.70 | 0.71 | 0.22 | 0.59 | 0.39 |

2016 | 0.59 | 0.64 | 0.08 | 0.42 | 0.49 |

2017 | 0.53 | 0.65 | 0.05 | 0.36 | 0.57 |

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**MDPI and ACS Style**

Gisolo, D.; Bevilacqua, I.; van Ramshorst, J.; Knohl, A.; Siebicke, L.; Previati, M.; Canone, D.; Ferraris, S.
Evapotranspiration of an Abandoned Grassland in the Italian Alps: Influence of Local Topography, Intra- and Inter-Annual Variability and Environmental Drivers. *Atmosphere* **2022**, *13*, 977.
https://doi.org/10.3390/atmos13060977

**AMA Style**

Gisolo D, Bevilacqua I, van Ramshorst J, Knohl A, Siebicke L, Previati M, Canone D, Ferraris S.
Evapotranspiration of an Abandoned Grassland in the Italian Alps: Influence of Local Topography, Intra- and Inter-Annual Variability and Environmental Drivers. *Atmosphere*. 2022; 13(6):977.
https://doi.org/10.3390/atmos13060977

**Chicago/Turabian Style**

Gisolo, Davide, Ivan Bevilacqua, Justus van Ramshorst, Alexander Knohl, Lukas Siebicke, Maurizio Previati, Davide Canone, and Stefano Ferraris.
2022. "Evapotranspiration of an Abandoned Grassland in the Italian Alps: Influence of Local Topography, Intra- and Inter-Annual Variability and Environmental Drivers" *Atmosphere* 13, no. 6: 977.
https://doi.org/10.3390/atmos13060977