PyEt - a Python package to estimate potential and reference evapotranspiration

Vremec, Matevž; Collenteur, Raoul

doi:10.5194/egusphere-egu21-15008

Cited by 11 publications

(8 citation statements)

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“…The meteorological indices (SPI and PDSI) were computed using the Python package ‘climate_indices’ used and maintained by NOAA (Adams, 2017). Potential evapotranspiration was computed using the Python package PyEt (Vremec & Collenteur, 2021).…”

Section: Methodsmentioning

confidence: 99%

Effects of dry spells on soil moisture and yield anomalies at a montane managed grassland site: A lysimeter climate experiment

et al. 2022

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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 CO 2 . 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.

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Section: Methodsmentioning

confidence: 99%

Effects of dry spells on soil moisture and yield anomalies at a montane managed grassland site: A lysimeter climate experiment

et al. 2022

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“…The product has been widely used in hydrological works over Europe [35][36][37][38] and offers a regional database which may standardize regional studies. Additionally, we used the temperature records from E-OBS to derive potential evapotranspiration (PET) using the Hargreaves formulation 39 and the pyet.py 40 Python module for computation. Each catchment therefore has 9 meterological time series associated with it (Table 3).…”

Section: Meteorological Recordsmentioning

confidence: 99%

EStreams: An Integrated Dataset and Catalogue of Streamflow, Hydro-Climatic Variables and Landscape Descriptors for Europe

M. do Nascimento,

Rudlang,

Höge

et al. 2024

Preprint

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Global hydrology datasets have become increasingly available, contributing to important scientific advances. However, in Europe, only a few such datasets were published and they only capture a fraction of the wealth of information national data providers have. We present “EStreams”, an extensive catalogue of openly available stream records and a dataset of hydro-climatic variables and landscape descriptors for 15,047 European catchments. The dataset spans up to 120 years of records, including catchment-aggregated hydro-climatic indices as well as landscape attributes (terrain, soils, lithology, vegetation and landcover). The catalogue includes detailed descriptions to allow users to directly access the sources of streamflow used, which overcomes data redistribution policies, different languages and structures of data portals. Furthermore, EStreams provides Python scripts for data retrieval, aggregation and processing. This approach enables users to readily and directly update their data as new records become available. Our goal is to extend current large-sample datasets and make a further step towards the integration of hydro-climatic and landscape data over Europe.

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“…Furthermore, the extension accommodated the continuous nature of interpolation calculations. Potential evapotranspiration (PET) values were calculated using the Penman method implemented in the pyet Python package [47]. The Penman method is a combination method, in which the total evaporation rate is calculated by weighing the evaporation rate due to net radiation and the evaporation rate due to mass transfer [48].…”

Section: Meteorological Datamentioning

confidence: 99%

Prediction of Inland Excess Water Inundations Using Machine Learning Algorithms

Kajári,

Tobak,

Túri

et al. 2024

Water

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Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of these inundations. Shallow “floods”, also defined as inland excess water, are phenomena that occur due to a complex set of interrelated factors. Our research presents a workflow based on active and passive satellite data from Sentinel-1 and -2, combined with a large auxiliary data set to detect and predict these floods. The workflow uses convolutional neural networks to classify water bodies based on Sentinel-1 and Sentinel-2 satellite data. The inundation data were complimented with meteorological, soil, land use, and GIS data to form 24 features that were used to train an XGBoost model and a deep neural network to predict future inundations, with a daily interval. The best prediction was reached with the XGBoost model, with an overall accuracy of 86%, a Kappa value of 0.71, and an F1 score of 0.86. The SHAP explainable AI method showed that the most important input features were the amount of water detected in the satellite imagery during the week before the forecast and during the period two weeks earlier, the number of water pixels in the surroundings on the day before the forecast, and the potential evapotranspiration on the day of the forecast. The resulting inland excess water inundation time series can be used for operational action, planning, and prevention.

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PyEt - a Python package to estimate potential and reference evapotranspiration

Cited by 11 publications

References 0 publications

Effects of dry spells on soil moisture and yield anomalies at a montane managed grassland site: A lysimeter climate experiment

Effects of dry spells on soil moisture and yield anomalies at a montane managed grassland site: A lysimeter climate experiment

EStreams: An Integrated Dataset and Catalogue of Streamflow, Hydro-Climatic Variables and Landscape Descriptors for Europe

Prediction of Inland Excess Water Inundations Using Machine Learning Algorithms

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