Evapotranspiration trends over the last 300 years reconstructed from historical weather station observations via machine learning

TL;DR

This study reconstructs 300 years of evapotranspiration trends using machine learning on historical weather data, revealing global increases and regional differences linked to climate change.

Contribution

It introduces a novel machine learning approach to extend evapotranspiration data back 300 years, providing insights into long-term water cycle changes.

Findings

Global ET increased since 1700, especially after 1900.

Higher ET increases in Northern Hemisphere mid-to-high latitudes.

Stable ET-precipitation correlation over centuries.

Abstract

Estimating historical evapotranspiration (ET) is essential for understanding the effects of climate change and human activities on the water cycle. This study used historical weather station data to reconstruct ET trends over the past 300 years with machine learning. A Random Forest model, trained on FLUXNET2015 flux stations' monthly data using precipitation, temperature, aridity index, and rooting depth as predictors, achieved an R2 of 0.66 and a KGE of 0.76 through 10-fold cross-validation. Applied to 5267 weather stations, the model produced monthly ET data showing a general increase in global ET from 1700 to the present, with a notable acceleration after 1900 due to warming. Regional differences were observed, with higher ET increases in mid-to-high latitudes of the Northern Hemisphere and decreases in some mid-to-low latitudes and the Southern Hemisphere. In drylands, ET and temperature were weakly correlated, while in humid areas, the correlation was much higher. The correlation between ET and precipitation has remained stable over the centuries. This study extends the ET data time span, providing valuable insights into long-term historical ET trends and their drivers, aiding in reassessing the impact of historical climate change and human activities on the water cycle and supporting future climate adaptation strategies.