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Assessing alternative agricultural water management strategies requires long-term field trials or vast data collection for model calibration and simulation.This work aims to assess whether an uncalibrated agro-hydrological model using global input datasets for climate, soil and crop information can serve as a decision support tool for crop water management under data scarcity.This study employs the Cool Farm Tool Water (CFTW) at eight eddy covariance sites of the FLUXNET2015 dataset. CFTW is tested using global (CFTWglobal) and local (CFTWlocal) input datasets under current and alternative management scenarios.Results show that the use of global datasets for estimating daily evapotranspiration had little effect on the median Root Mean Square Error (RMSE) (CFTWglobal: 1.70 mm, CFTWlocal: 1.79 mm), while, however, the median model bias is much greater (CFTWglobal: −18.6%, CFTWlocal: −4.3%). Furthermore, the periods of water stress were little affected by the use of local or global data (median accuracy: 0.84), whereas the use of global data inputs led to a significant overestimation of irrigation water requirements (median difference: 110 mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local reference evapotranspiration and soil for some European growing seasons.We identify model outputs that can support decision-making when relying on global data, such as periods of water stress and the daily dynamics of water use. However, our findings also emphasize the difficulty of overcoming data scarcity in decision-making in agricultural water management. Furthermore, we provide recommendations for enhancing model performance and thus may increase the accessibility of reliable decision support tools in the future.

Abstract Spaceborne impact ionization mass spectrometers, such as the Cosmic Dust Analyzer on board the past Cassini spacecraft or the SUrface Dust Analyzer being built for NASA's upcoming Europa Clipper mission, are of crucial importance for the exploration of icy moons in the Solar System, such as Saturn's moon Enceladus or Jupiter's moon Europa. For the interpretation of data produced by these instruments, analogue experiments on Earth are essential. To date, thousands of laboratory mass spectra have been recorded with an analogue experiment for impact ionization mass spectrometers. Simulation of mass spectra of ice grains in space is achieved by a Laser Induced Liquid Beam Ion Desorption (LILBID) approach. The desorbed cations or anions are analyzed in a time‐of‐flight mass spectrometer. The amount of unstructured raw data is increasingly challenging to sort, process, interpret and compare with data from space. Thus far this has been achieved manually for individual mass spectra because no database containing the recorded reference spectra was available. Here we describe the development of a comprehensive, extendable database containing cation and anion mass spectra from the laboratory LILBID facility. The database is based on a Relational Database Management System with a web server interface and enables filtering of the laboratory data using a wide range of parameters. The mass spectra can be compared not only with data from past and future space missions but also mass spectral data generated by other, terrestrial, techniques. The validated and approved subset of the database is available for general public (https://lilbid-db.planet.fu-berlin.de).