Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Wu, Haijiang; Su, Xiaoling; Singh, Vijay P.; Feng, Kai; Niu, Jiping

doi:10.1029/2021wr029562

Cited by 57 publications

(41 citation statements)

References 79 publications

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“…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.…”

Section: Methodsmentioning

confidence: 99%

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

Singh

2021

Geophysical Research Letters

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The amplifying effects of disasters caused by compound dry and hot extremes (dry‐hot events) have attracted widespread attention. This study presents a novel Blended Dry and Hot Events Index (BDHI) considering various dry and hot conditions (i.e., dry/hot, dry/cool, wet/hot, and wet/cool conditions). BDHI was applied to monitor dry‐hot events over global land areas during the 1950–2019 period and its performance was compared against that of the Standardized Compound Event Indicator (SCEI), which includes certain unsatisfactory or unclear features. We found that BDHI comprehensively reflected the characteristics of meteorological drought and temperature anomalies in monitoring dry‐hot events. Many regions around the globe have been experiencing more severe dry‐hot events since the 1990s, especially Russia, the USA, China, India, Australia, southern Africa, Europe, and South America. The proposed index is expected to serve as a potential tool for monitoring dry‐hot events and will be useful for managing and mitigating associated risks.

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Section: Methodsmentioning

confidence: 99%

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

Singh

2021

Geophysical Research Letters

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“…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).…”

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confidence: 99%

“…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).…”

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confidence: 99%

“…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.…”

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confidence: 99%

“…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.…”

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

Singh

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Agricultural drought mainly stems from reduced soil moisture and precipitation, and it causes adverse impacts on the growth of crops and vegetation, thereby affecting agricultural production and food security. In order to develop drought mitigation measures, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas in three dimensions (3C-vine model) in which antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Furthermore, a meta-Gaussian (MG) model was selected as a reference to evaluate the forecast skill. The agricultural drought in China in August of 2018 was selected as a typical case study, and the spatial patterns of 1- to 3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the good predictive ability of the model. The performance metrics – the Nash–Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the root-mean-square error (RMSE) – showed that the 3C-vine model outperformed the MG model with respect to forecasting agricultural drought in August for diverse lead times. Moreover, the 3C-vine model exhibited excellent forecast skill with respect to capturing the extreme agricultural drought over different selected typical regions. This study may help to guide drought early warning, drought mitigation, and water resource scheduling.

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Establish an agricultural drought index that is independent of historical element probabilities

Pan,

Xiao,

Pan

2024

Meteorological Applications

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Currently, there are three main shortcomings in meteorological drought indices: first, they rely on historical climate probability functions; second, the timescale used in calculations has a certain degree of subjectivity; third, the same index value may correspond to vastly different levels of actual drought in different climate types of regions. The purpose of this article is to establish a meteorological drought index that does not rely on historical meteorological element probability functions. Through theoretical derivation, four drought‐level maintenance lines are established on the cumulative precipitation‐cumulative water surface evaporation coordinate plane, and the coordinate quadrant is divided into five drought‐level areas. Through forward daily rolling accumulation, the maximum distance point is selected from the dynamically changing coordinate points to determine the corresponding cumulative precipitation and cumulative evaporation. The meteorological drought index is established by the distance from the selected coordinate point to each drought‐level maintenance line. Using daily precipitation and evaporation data from meteorological observation stations, the index is calculated based on the established meteorological drought index model, and compared with actual drought evolution and drought disaster records. The results show that the index can capture the development of drought well, and its changes are very consistent with drought disaster records. The index is of great significance for drought monitoring or assessment, and can provide guidance for water resource allocation, crop layout, and urban planning. Furthermore, it can also provide a way of thinking that does not rely on historical element probabilities for future drought research.

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Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Cited by 57 publications

References 79 publications

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

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

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

Establish an agricultural drought index that is independent of historical element probabilities

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