The rise of compound warm-season droughts

In the last decades, the impactful European warm-season droughts have been steadily increasing, substituting other drought types.

An international team of scientists led by CZU Prague investigated how European droughts have changed since 1900. They discovered a substantial increase in the number of short warm-season droughts in the largest part of Europe co-occurring with high spring/summer temperatures. Characterized by their rapid development and strong intensity, these warm-season droughts have dominated the first decades of the 21st century in Europe.

The authors used a computer hydrological model developed by UFZ to simulate drought and applied a machine learning algorithm to identify characterize changes to droughts. Among the findings, the authors reported that these droughts are steadily replacing longer duration and less intense droughts. The study reveals that during warm-season droughts the rate of soil moisture depletion is very high, resulting in widespread impacts on agriculture, and the natural ecosystem.

If the increasing trend of warm-season drought in Europe continues, we might also observe a raise in agricultural water demand and wildfires, as well as changes to vegetation regimes. Following the precautionary principle, the alarming trend in warm-season drought should be considered in water resources and natural hazards risk assessment, mitigation and management policies.

This work was carried out as part of the bilateral Project XEROS (eXtreme EuRopean drOughtS: multimodel synthesis of past, present and future events), funded by Czech Science Foundation and the Deutsche Forschungsgemeinschaft.

Publication:

Y. Markonis, R. Kumar, M. Hanel, O. Rakovec, P. Maca, A. AghaKouchak, The rise of compound warm-season droughts in Europe. Sci. Adv. 7, eabb9668 (2021).

Fig. 1 Classification of drought events. (A) The mHM simulation produces time series of runoff and soil moisture using precipitation and temperature at each grid cell (50 × 50 km). (B) Three types of drought (meteorologic, hydrological, and agricultural) are determined, and the events that last for at least 3 months are assessed for each grid cell. (C) The machine learning classification scheme reduces the dimensionality of the issue to three major drought classes for NEU, CEU, and MED. The borders of the three regions are presented in fig. S1. Each pointed line represents the number of grid cells under drought due to deficit in precipitation (P), runoff (Q), and soil moisture (SM) or excess PET [colors are as in (A)]. (D) The temporal evolution of annual drought coverage per class and region highlights current drought dynamics (loess regression).Fig. 1 Classification of drought events. (A) The mHM simulation produces time series of runoff and soil moisture using precipitation and temperature at each grid cell (50 × 50 km). (B) Three types of drought (meteorologic, hydrological, and agricultural) are determined, and the events that last for at least 3 months are assessed for each grid cell. (C) The machine learning classification scheme reduces the dimensionality of the issue to three major drought classes for NEU, CEU, and MED. The borders of the three regions are presented in fig. S1. Each pointed line represents the number of grid cells under drought due to deficit in precipitation (P), runoff (Q), and soil moisture (SM) or excess PET [colors are as in (A)]. (D) The temporal evolution of annual drought coverage per class and region highlights current drought dynamics (loess regression). 

 

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