Evapotranspiration dataset of 31 plant species and an unplanted control based on high-resolution weighing data, 2023–2025
Description
This dataset contains high-resolution weighing data and derived evapotranspiration estimates for 31 planted species and one unplanted control. The raw data were recorded at 5-minute intervals from outdoor weighing units and processed in R to generate cleaned weight time series, daily evapotranspiration rates, hourly evapotranspiration rates, and daily sums derived from hourly evapotranspiration. The experiment includes the following plant species: Anthericum liliago, Hakonechloa macra, Festuca rupicola, Galatella linosyris, Origanum vulgare, Achnatherum calamagrostis, Koeleria glauca, Thymus serpyllum, Helictotrichon sempervirens, Sedum album, Leucanthemum vulgare, Carex pendula, Pennisetum alopecuroides, Festuca mairei, Prunella grandiflora, Campanula poscharskyana, Miscanthus sinensis, Carex montana, Sanguisorba minor, Achillea millefolium, Hieracium pilosella, Panicum virgatum, Dianthus deltoides, Malva moschata, Acinos alpinus, Dianthus carthusianorum, Sesleria autumnalis, Antennaria dioica, Geranium sanguineum, Armeria maritima, Galium verum, and an unplanted control.The weight data remain in their original unit of grams per basket in the cleaned 5-minute dataset. Conversion to evapotranspiration units is performed only during the calculation of evapotranspiration. Daily evapotranspiration was calculated from the difference between the mean weight at the beginning and end of each day and converted to L m-² using a surface area of 0.1 m². Hourly evapotranspiration was calculated from differences between consecutive full-hour weight measurements and converted to mL m-². The analysis refers to meteorological variables from the German Weather Service (DWD), including air temperature, windspeed, shortwave solar radiation and relative humidity for Wuerzburg, Germany. The data were used to analyse species-specific evapotranspiration patterns, seasonal variation, evapotranspiration under temperature extremes, irrigation responses, drought-period behaviour, and the effects of environmental predictors on evapotranspiration using mixed-effects models with temporal autocorrelation structures.
Files
Steps to reproduce
see R-Script
Institutions
- Center for Applied Energy ResearchBavaria, Würzburg