Soil water sustains terrestrial life, yet its fate is uncertain under a changing climate. We conducted a deuterium labeling experiment to determine whether elevated atmospheric carbon dioxide (CO ₂ ), warming, and drought impact soil water storage and transport in a temperate grassland. Elevated CO ₂ created a wetter rootzone compared with ambient conditions, whereas warming decreased soil moisture. Soil water remained well mixed in all global change treatments except for summer drought combined with warming and elevated CO ₂ . These combined treatments caused the grassland to conserve water and restricted soil water flow to large, rapidly draining pores without mixing with small, slowly draining pores. Our results suggest that drought in a warmer, more CO ₂ -rich climate can severely alter grassland ecohydrology by constraining postdrought soil water flow and grassland water use.

Effects of climate change on the ecosystem productivity and water fluxes have been studied in various types of experiments. However, it is still largely unknown whether and how the experimental approach itself affects the results of such studies. We employed two contrasting experimental approaches, using high-precision weighable monolithic lysimeters, over a period of 4 years to identify and compare the responses of water fluxes and aboveground biomass to climate change in permanent grassland. The first, manipulative, approach is based on controlled increases of atmospheric CO₂ concentration and surface temperature. The second, observational, approach uses data from a space-for-time substitution along a gradient of climatic conditions. The Budyko framework was used to identify if the soil ecosystem is energy limited or water limited. Elevated temperature reduced the amount of non-rainfall water, particularly during the growing season in both approaches. In energy-limited grassland ecosystems, elevated temperature increased the actual evapotranspiration and decreased aboveground biomass. As a consequence, elevated temperature led to decreasing seepage rates in energy-limited systems. Under water-limited conditions in dry periods, elevated temperature aggravated water stress and, thus, resulted in reduced actual evapotranspiration. The already small seepage rates of the drier soils remained almost unaffected under these conditions compared to soils under wetter conditions. Elevated atmospheric CO₂ reduced both actual evapotranspiration and aboveground biomass in the manipulative experiment and, therefore, led to a clear increase and change in seasonality of seepage. As expected, the aboveground biomass productivity and ecosystem efficiency indicators of the water-limited ecosystems were negatively correlated with an increase in aridity, while the trend was unclear for the energy-limited ecosystems. In both experimental approaches, the responses of soil water fluxes and biomass production mainly depend on the ecosystems' status with respect to energy or water limitation. To thoroughly understand the ecosystem response to climate change and be able to identify tipping points, experiments need to embrace sufficiently extreme boundary conditions and explore responses to individual and multiple drivers, such as temperature, CO₂ concentration, and precipitation, including non-rainfall water. In this regard, manipulative and observational climate change experiments complement one another and, thus, should be combined in the investigation of climate change effects on grassland.

Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncertainty, as the ET estimated by each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of 20 different PET methods on both time series and gridded datasets. The majority of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with 20 PET methods for station and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO₂ concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity and uncertainty analyses, and hindcasting and forecasting studies (especially in data-scarce regions) is provided.

Evapotranspiration (ET) is a major component of the hydrological cycle and accurate estimates of the flux are important to the water and agricultural sector, among others. Due to difficulties in the direct observation of ET in the field, the flux is often estimated from other meteorological data using empirical formulas. There is a wide variety of such formulas, with different levels of input data and parameter requirements. While some Python packages are available in the Python ecosystem for these tasks, they typically focus on one specific formula or data type. The goal of PyEt is to provide a Python package for the estimation of ET that works with many different data types, is well documented and tested, and simple to use. The source code is hosted at GitHub (https://github.com/phydrus/PyEt) and Pypi can be used to install the package. PyEt currently contains nine different methods to estimate ET and various methods to estimate surface and aerodynamic resistance. The methods are tested against other open source data to ensure proper functioning of the methods. While the methods currently are only implemented for 1D data (e.g. time series data), future work will focus on enabling the methods on 2D and 3D data as well (such as Numpy Arrays, XArray, and NetCDF files). The package allows hydrologists to compute and compare evapotranspiration estimates using different approaches with minimum effort. The presentation will focus on the problems associated with reproducibility in ET estimation and linkage with existing Python libraries to perform complex sensitivity and uncertainty analyses.

Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the used formula, adding uncertainty to the outcomes of hydrological models using ET. Climate change may cause additional uncertainty as each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change, and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of twenty different PET methods on both time series (Pandas) and gridded datasets (xarray). Most of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with twenty PET methods for station, and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO₂ concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity/uncertainty analyses, and hind/forecasting studies, especially in data-scarce regions, is provided.