In:
Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 26, No. 12 ( 2022-06-24), p. 3241-3261
Abstract:
Abstract. The term “flash drought” describes a type of drought
with rapid onset and strong intensity, which is co-affected by both
water-limited and energy-limited conditions. It has aroused widespread
attention in related research communities due to its devastating impacts on
agricultural production and natural systems. Based on a global reanalysis
dataset, we identify flash droughts across China during 1979–2016 by focusing on the depletion rate of weekly soil moisture percentile.
The relationship between the rate of intensification (RI) and nine related
climate variables is constructed using three machine learning (ML)
technologies, namely, multiple linear regression (MLR), long short-term
memory (LSTM), and random forest (RF) models. On this basis, the
capabilities of these algorithms in estimating RI and detecting droughts (flash
droughts and traditional slowly evolving droughts) were analyzed.
Results showed that the RF model achieved the highest skill in terms of RI
estimation and flash drought identification among the three approaches.
Spatially, the RF-based RI performed best in southeastern China, with an
average CC of 0.90 and average RMSE of the 2.6 percentile per week, while poor
performances were found in the Xinjiang region. For drought detection, all
three ML technologies presented a better performance in monitoring flash
droughts than in conventional slowly evolving droughts. Particularly, the
probability of detection (POD), false alarm ratio (FAR), and critical
success index (CSI) of flash drought derived from RF were 0.93, 0.15, and
0.80, respectively, indicating that RF technology is preferable in estimating
the RI and monitoring flash droughts by considering multiple meteorological
variable anomalies in adjacent weeks to drought onset. In terms of the
meteorological driving mechanism of flash drought, the negative
precipitation (P) anomalies and positive potential evapotranspiration (PET)
anomalies exhibited a stronger synergistic effect on flash droughts compared
to slowly developing droughts, along with asymmetrical compound influences
in different regions of China. For the Xinjiang region, P deficit played a
dominant role in triggering the onset of flash droughts, while in
southwestern China, the lack of precipitation and enhanced evaporative
demand almost contributed equally to the occurrence of flash drought. This
study is valuable to enhance the understanding of flash droughts and
highlight the potential of ML technologies in flash drought monitoring.
Type of Medium:
Online Resource
ISSN:
1607-7938
DOI:
10.5194/hess-26-3241-2022
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2022
detail.hit.zdb_id:
2100610-6
Permalink
