Model Comparisons Between Canonical Vine Copulas and Meta-Gaussian for Agricultural Drought Forecasting over China

Agricultural drought is caused by reduced soil moisture and precipitation and affects the growth of crops and vegetation, and in turn agricultural production and food security. For developing measures for drought mitigation, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas under three-dimensions (3C-vine model), in which the antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Besides, the meta-Gaussian (MG) model was selected as a reference model to evaluate the forecast skill. The agricultural drought in August of 2018 was selected as a case study, and the spatial patterns of 1–3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the predictive ability of the model. The performance metrics (NSE, R2, and RMSE) showed that the 3C-vine model outperformed the MG model for August under diverse lead times. Also, the 3C-vine model exhibited excellent forecast skills in capturing the extreme agricultural drought over different selected typical regions. This study may help with drought early warning, drought mitigation, and water resources scheduling.

The Relationship between Economic Growth, Small and Medium Enterprises and the Number of Employees in Malaysia using Vine Copula Approach

This paper investigates the dynamic linkages of dependency between economic growth, the development of small and medium enterprises (SMEs) in Malaysia represented by SMEs’ value-added, and the number of employees in Malaysia, shown by the total number of SMEs’ employees in Malaysia as an additional variable. A vine copula methods was used to assess the linkage between these three variables by utilizing the panel data sets from 1998-2017. The results confirmed that dependency modeling, using vine copulas, is useful for high dimensional distributions of binding affinity between these variables. Vine copulas provide a versatile and flexible approach to the study. The results show that there is a strong positive correlation among the variables. The dependence between the SMEs’ growth and the total number of employees is the strongest, followed by the dependence between SMEs’ growth and economic growth. This study also found that the interrelation among the variables and reliance structure is also influenced by the country’s economic system and situation.

Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

Reliable estimates of missing streamflow values are relevant for water resource planning and management. This study proposes a multiple-dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high-dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple-dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin–Pee Dee River basin in the eastern USA. Both results inform that the proposed model is better suited for infilling missing values. To be specific, the proposed multiple-dependence model shows the improvement of 9.2 % on average compared to the bivariate model from the historical case study. The performance of the vine copula is further compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.