Abstract
With the intensification of climate change, the coupling effect between climate variables plays an important role in meteorological drought identification. However, little is known about the contribution of climate variables to drought development. This study constructed four scenarios using the random forest model during 1981–2016 in the Luanhe River Basin (LRB) and quantitatively revealed the contribution of climate variables (precipitation; temperature; wind speed; solar radiation; relative humidity; and evaporative demand) to drought indices and drought characteristics, that is, the Standard Precipitation Evapotranspiration Index (SPEI), Standard Precipitation Index (SPI), and Evaporative Demand Drought Index (EDDI). The result showed that the R2 of the model is above 0.88, and the performance of the model is good. The coupling between climate variables can not only amplify drought characteristics but also lead to the SPEI, SPI, and EDDI showing different drought states when identifying drought. With the decrease in timescale, the drought intensity of the three drought indices became stronger and the drought duration shortened, but the drought frequency increased. For short-term drought (1 mon), four scenarios displayed that the SPEI and SPI can identify more drought events. On the contrary, compared with the SPEI and SPI, the EDDI can identify long and serious drought events. This is mainly due to the coupling of evaporative demand, solar radiation, and wind speed. Evaporation demand also contributed to the SPEI, but the contribution (6–13%) was much less than the EDDI (45–85%). For SPEI-1, SPEI-3, and SPEI-6, the effect of temperature cannot be ignored. These results are helpful to understand and describe drought events for drought risk management under the condition of global warming.
Highlights
Drought is one of the most serious natural disasters affecting the development of human society (Deb et al, 2019)
We found that the Evaporative Demand Drought Index (EDDI), Standard Precipitation Evapotranspiration Index (SPEI), and Standard Precipitation Index (SPI) showed the opposite states when it came to drought recognition (Figure 2D)
In 2002, situation 1 (S1) of Figure 2D showed that the EDDI identified the dry state, while the SPEI and SPI identified the wet state in April (Figure 6A)
Summary
Drought is one of the most serious natural disasters affecting the development of human society (Deb et al, 2019). Meteorological drought, in general, precedes other droughts and is defined according to the degree of the lack of precipitation in an area over some time. One of the most prominent and widespread concerns is regional drought caused by climate warming and precipitation change, which has caused serious disasters worldwide (Zhai et al, 2010). In 2014, California experienced a serious drought event that was mainly caused by an extreme lack of precipitation. Coupling Contribution of Climate Variables and high temperature and was a record-breaking event in the last century (Griffin and Anchukaitis, 2014). In 2011, high temperatures and low soil moisture in Texas aggravated drought events, and rainfall was extremely scarce (Karl et al, 2012). It is estimated that drought in California and Texas caused economic losses of $2.7 billion and $7.7 billion, respectively (Shukla et al, 2015). A better understanding of drought characteristics and their physical variable is significant for monitoring and forecasting drought
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