Abstract
Abstract. As an essential component of drought risk, crop–drought vulnerability refers to the degree of the adverse response of a crop to a drought event. Different drought intensities and environments can cause significant differences in crop yield losses. Therefore, quantifying drought vulnerability and then identifying its spatial characteristics will help understand vulnerability and develop risk-reduction strategies. We select the European winter wheat growing area as the study area and 0.5∘ × 0.5∘ grids as the basic assessment units. Winter wheat drought vulnerability curves are established based on the erosion–productivity impact calculator model simulation. Their loss change and loss extent characteristics are quantitatively analysed by the key points and cumulative loss rate, respectively, and are then synthetically identified via K-means clustering. The results show the following. (1) The regional yield loss rate starts to rapidly increase from 0.13 when the drought index reaches 0.18 and then converts to a relatively stable stage with the value of 0.74 when the drought index reaches 0.66. (2) In contrast to the Pod Plain, the stage transitions of the vulnerability curve lags behind in the southern mountain area, indicating a stronger tolerance to drought. (3) According to the loss characteristics during the initial, development, and attenuation stages, the vulnerability curves can be divided into five clusters, namely low-low-low, low-low-medium, medium-medium-medium, high-high-high, and low-medium-high loss types, corresponding to the spatial distribution from low latitude to high latitude and from mountain to plain. The paper provides ideas for the study of the impact of environment on vulnerability and for the possible application of vulnerability curve in the context of climate change.
Highlights
Drought is a widespread natural disaster causing the largest agricultural losses in the world
The R2 of the regional vulnerability curve fitted by all the grid drought index (DI)–loss rate (LR) samples is 0.90 and the root-mean-square error (RMSE) is 0.12, indicating a high overall goodness of fit
To further explore the relationship between the vulnerability characteristic distribution and environmental variables, Spearman correlation analysis is performed between the vulnerability characteristic parameters (DI1, DI2, DI3, and cumulative loss rate (CLR)) and environmental variables
Summary
Drought is a widespread natural disaster causing the largest agricultural losses in the world. More than one-half of the earth is susceptible to drought, including most of the major agricultural areas (Kogan, 1997). How to assess and manage agricultural drought risks has become a focus of the world (Reid et al, 2006; Li et al, 2009; Mishra and Singh, 2010). Crop drought vulnerability assessment focuses on crops, the biophysical factors closely related to crop growth processes (González Tánago et al, 2016; Wu et al, 2017), describing the damage to crops caused by different intensity hits. Crop drought vulnerability assessment methods mainly include the following three aspects
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