Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Abstract

Monitoring and prediction of agricultural drought are paramount to food security at the global and regional scales, particularly under the influence of climate change and anthropogenic activities. Soil moisture is an effective indicator for monitoring and characterizing agricultural drought. Soil moisture (agricultural drought) is mainly affected by precipitation (meteorological drought) and temperature (hot conditions). Owing to the flexibility of vine copulas in handling multidimensional variables by decomposing them into pair copula constructions (PCCs), we propose a novel drought prediction method using three predictors, namely antecedent meteorological drought, previous hot conditions, and persistent agricultural drought, based on the conditional distributions of C‐vine copulas in a four‐dimensional scenario. The proposed model was applied to agricultural drought (characterized by the standardized soil moisture index (SSI)) prediction with 1–2‐months lead time for the summer season (i.e., August at a 6‐months timescale) in China. Taking two severe agricultural drought events that occurred in many regions across China in August of 2006 and 2014 as validation cases, the SSI predictions with 1–2‐months lead time using the conditional C‐vine copulas model were found to be generally consistent with the corresponding historical SSI observations in most parts of China. Performance evaluation using the Nash‐Sutcliffe efficiency (NSE), coefficient of determination (R2), and F1 score (F1S) for different climate regions also indicated that this model provided a reliable prediction of agricultural drought for most areas of China. The outcome of this study can serve as a guidance for drought prediction, early warning, and drought mitigation.

Blended Dry and Hot Events Index for Monitoring Dry‐Hot Events Over Global Land Areas

“…The gridded monthly precipitation and maximum temperature data with a 0.5° spatial resolution are provided by CRU for the 1950–2019 period. The ERA5 reanalysis and CRU observational data sets have been extensively used in drought monitoring, heatwave tracking, and climate model validation (e.g., Liu et al., 2020; Mukherjee & Mishra, 2021; Wang & Yuan, 2021; Weber et al., 2020; Wu et al., 2021). This study specifically focused on dry‐hot events during the June‐July‐August (JJA) and December‐January‐February (DJF) periods.…”

Comparison between canonical vine copulas and a meta-Gaussian model for forecasting agricultural drought over China

“…Zhang et al, 2021). Under natural conditions, atmospheric precipitation is a paramount source for the replenishment of soil moisture (Wu et al, 2021b). Therefore, reduced soil moisture (agricultural drought) mainly arises from a precipitation deficit (meteorological drought) (Modanesi et al, 2020;Orth and Destouni, 2018).…”

“…Zhang et al, 2021T. Zhang et al, , 2022Zhou et al, 2019), flood and runoff forecasting (Bevacqua et al, 2017b;Hemri et al, 2015;Liu et al, 2018;, and drought forecasting (Ganguli and Reddy, 2014;Wu et al, 2021b). However, when bivariate copulas are extended to higher-dimensional (≥ three-dimensional) cases, they are restricted due to the nonexistence of analytical expressions (Liu et al, 2021a).…”

“…However, when bivariate copulas are extended to higher-dimensional (≥ three-dimensional) cases, they are restricted due to the nonexistence of analytical expressions (Liu et al, 2021a). Symmetric Archimedean copulas and nested Archimedean copulas have partially addressed the issues of dimensionality, but characterizing the various dependence structures with single-parameter and Archimedean class copulas is difficult (Aas and Berg, 2009;Hao et al, 2016;Wu et al, 2021b). For-tunately, vine copulas, developed by Joe (1996) and Bedford and Cooke (2002), can be adopted to address these limitations.…”

“…These copulas have been extensively applied in the hydrological field (Bevacqua et al, 2017b;Liu et al, 2021b;Vernieuwe et al, 2015;Wu et al, 2021a, b). For instance, Xiong et al (2014) derived annual runoff distributions using canonical vine copulas; Liu et al (2018) developed a framework to investigate compound floods based on canonical vine copulas; Wang et al (2019) utilized regular vine copulas with historical streamflow and climate drivers to simulate monthly streamflow for the headwater catchment of the Yellow River basin; Liu et al (2021a) developed a hybrid ensemble forecast model, using Bayesian model averaging combined canonical vine copulas, to forecast water level; and Wu et al (2021b) proposed an agricultural drought forecast model based on vine copulas using four-dimensional scenarios.…”