Probabilistic modelling of the dependence between rainfed crops and drought hazard

Abstract

Abstract. Extreme weather events, such as droughts, have been increasingly affecting the agricultural sector, causing several socio-economic consequences. The growing economy requires improved assessments of drought-related impacts in agriculture, particularly under a climate that is getting drier and warmer. This work proposes a probabilistic model that is intended to contribute to the agricultural drought risk management in rainfed cropping systems. Our methodology is based on a bivariate copula approach using elliptical and Archimedean copulas, the application of which is quite recent in agrometeorological studies. In this work we use copulas to model joint probability distributions describing the amount of dependence between drought conditions and crop yield anomalies. Afterwards, we use the established copula models to simulate pairs of yield anomalies and drought hazard, preserving their dependence structure to further estimate the probability of crop loss. In the first step, we analyse the probability of crop loss without distinguishing the class of drought, and in the second step we compare the probability of crop loss under drought and non-drought conditions. The results indicate that, in general, Archimedean copulas provide the best statistical fits of the joint probability distributions, suggesting a dependence among extreme values of rainfed cereal yield anomalies and drought indicators. Moreover, the estimated conditional probabilities suggest that when drought conditions are below moderate thresholds, the risk of crop loss increases between 32.53 % (cluster 1) and 32.6 % (cluster 2) in the case of wheat and between 31.63 % (cluster 2) and 55.55 % (cluster 2) in the case of barley. From an operational point of view, the results aim to contribute to the decision-making process in agricultural practices.

Risk of crop failure due to compound dry and hot extremes estimated with nested copulas

“…Higher probabilities of crop loss under drought and/or heat stress are generally expected in the southern region of Spain, in comparison to the northern region ( Figures 6 and 7), in agreement with the higher temperatures and lower rainfall amounts observed in the south (Ribeiro et al, 2019a;IM and AEMET, 2011). In the case of wheat losses, this finding is in agreement with previous work which focused on drought risks for the same crops and the same region (assessed based on remote sensing and hydro-meteorological drought indicators, Ribeiro et al (2019b). However, Ribeiro et al (2019b) identified a higher likelihood of barley loss with drought in the northern cluster.…”

“…In the case of wheat losses, this finding is in agreement with previous work which focused on drought risks for the same crops and the same region (assessed based on remote sensing and hydro-meteorological drought indicators, Ribeiro et al (2019b). However, Ribeiro et al (2019b) identified a higher likelihood of barley loss with drought in the northern cluster. This discrepancy underlines importance of addressing the interaction between compound dry and hot conditions and the associated impacts on vegetation.…”

Risk of crop failure due to compound dry and hot extremes estimated with nested copulas

“…Those nine provinces were aggregated into two distinct regions ( Fig. 1), which are dominated by rainfed agricultural practices following the nonirrigated arable land classification from the CORINE Land Cover dataset based on an earlier regionalisation (Ribeiro et al, 2019c;Ribeiro et al, 2019b). The provincial regionalisation consisted in the application of three main criteria: first the provinces with land use dominated by agricultural practices were identified (Fig.…”

“…Figure 2 suggests that the greatest influence of P and T max in crop yields is observed during spring (MAM in both regions and cereals), corresponding to the reproductive phase of plant development, when vegetation is photosynthetically more active. Therefore, for the remaining analysis we focus on 3-monthly means of T max and 3-monthly means of P during spring (P MAM and T max MAM , respectively), which has also been identified in previous studies as a growth stage sensitive to the effects of water content and high temperatures (Ferrise et al, 2011;Del Moral et al, 2003;Iglesias and Quiroga, 2007;Ribeiro et al, 2019c). This selection of climate variables allows for the maximisation of the dependence between climate conditions and yields as also shown by previous work based on the same data (Ribeiro et al, 2019c).…”

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

“…Thus, regional analysis has proven to be more efficient for drought management than the punctual approach [35,36]. Regionalization techniques are essential to reduce random fluctuations of a point-based approach and homogenizing drought analysis [37][38][39]. Clustering techniques that consider the point-wise correlation of a temporal series are essential and are increasing in use in hydrological applications [16,[40][41][42][43].…”