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Philipp Forchheimer must be considered one of the most important founding fathers of modern geohydrology. Connected to his name are the Forchheimer law, which describes non-Darcian flow in porous media, and the Dupuit-Forchheimer groundwater flow assumption. Probably unbeknownst to most current hydrologists, he introduced many more important concepts, which still are cornerstones of our field of study today. The most important is the Laplace equation, which forms the basis of all quantitative modelling of groundwater flow. Flow nets, the method of images and the refraction of flowpaths at layer boundaries are further concepts useful today. Through his work in several countries, many papers and textbooks on hydraulics, the latter translated into several languages, and, finally, his connection with the famous Vienna school of soil mechanics, he left a scientific legacy that is very much alive, although many are unaware that it goes back to Philipp Forchheimer. [ABSTRACT FROM AUTHOR]

Biodiversity, the source of origin, and ecological roles of fungi in groundwater are to this day a largely neglected field in fungal and freshwater ecology. We used DNA-based Illumina high-throughput sequence analysis of both fungal gene markers 5.8S and internal transcribed spacers region 2 (ITS2), improving taxonomic classification. This study focused on the groundwater and river mycobiome along an altitudinal and longitudinal transect of a pre-alpine valley in Austria in two seasons. Using Bayesian network modeling approaches, we identified patterns in fungal community assemblages that were mostly shaped by differences in landscape (climatic, topological, and geological) and environmental conditions. While river fungi were comparatively more diverse, unique fungal assemblages could be recovered from groundwater, including typical aquatic lineages such as Rozellomycota and Olpidiomycota. The most specious assemblages in groundwater were not linked to the input of organic material from the surface, and as such, seem to be sustained by characteristic groundwater conditions. Based on what is known from closely related fungi, our results suggest that the present fungal communities potentially contribute to mineral weathering, carbon cycling, and denitrification in groundwater. Furthermore, we were able to observe the effects of varying land cover due to agricultural practices on fungal biodiversity in groundwater ecosystems. This study contributes to improving our understanding of fungi in the subsurface aquatic biogeosphere. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The frequency and severity of droughts in the Alps are expected to increase due to rising air temperatures and changes in precipitation regimes. Although biomass production in humid mountain areas tends to be energy limited rather than water limited, an increase in droughts may have negative impacts on the water availability and thus agricultural yields. This study aimed to analyse the impacts of dry spells on soil moisture and yield anomalies at a montane permanent grassland site in Austria. Dry spells in the time period from 2018 to 2020 were identified using the Standardized Precipitation Index, Palmer Drought Severity Index and the Soil Moisture Anomaly Index. Data from a lysimeter climate experiment were used to evaluate drought impacts on soil water storage and grassland yield under ambient and manipulated conditions. The results indicated the occurrence of three extreme droughts between 2018 and 2020. Although the studied grassland is generally considered a nonwater‐limited ecosystem, the most extreme drought in summer 2019 caused severe and extreme yield anomalies under ambient and heated conditions. Only mild yield anomalies were observed on plots with elevated atmospheric carbon dioxide concentration. This drought‐mitigating effect was attributed to the water savings enabled by partial stomatal closure under elevated CO2. The shorter dry spells in spring and late summer 2018 led to more diverse effects; mildly to moderately negative yield anomalies were found on the heated plots, whereas the anomalies tended to be less negative or even positive on plots under ambient temperature. In contrast, some time periods without water stress showed positive effects of heating on yield. These findings suggest that drought impacts on a humid montane grassland depend on both water availability and air temperature. Higher air temperature can have positive effects on yield if the ecosystem is energy limited. However, global warming suggests a tendency from energy to water limitation, in which the increased evaporative demand of the atmosphere aggravates soil moisture droughts and thus has potentially negative effects on yield. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]