<p>Compound temperature–precipitation shocks shape hydro-climatic impacts in semi-arid regions but are not fully represented by linear correlation. Using monthly observations from Tulcea, Dobrogea (1965–2019), we quantify contemporaneous dependence by isolating independent and identically distributed innovation shocks and modeling their joint distribution with copulas. Temperature is filtered with a periodic autoregressive moving average of orders 2 and 1 and a periodic Generalized Autoregressive Conditional Heteroskedasticity variance model, and fitted with a Student-<i>t</i> marginal. For precipitation, we use a periodic autoregressive of first-order model with a Generalized Extreme Value marginal. Probability-integral transforms (PIT) yield uniform innovations. Candidate copulas are estimated by maximum likelihood, selected via Akaike/Bayesian criteria (AIC/BIC), and checked with White’s information-matrix test. The Frank copula is selected for all four compound configurations (warm–dry, cold–wet, warm–wet, cold–dry), implying a zero asymptotic tail dependence but non-trivial finite quantile <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\left( {\tilde{t} \in \left( {0,1} \right)} \right)$</EquationSource> <EquationSource Format="MATHML"><math> <mfenced close=")" open="("> <mrow> <mover accent="true"> <mi>t</mi> <mo stretchy="false">~</mo> </mover> <mo>∈</mo> <mfenced close=")" open="("> <mrow> <mn>0</mn> <mo>,</mo> <mn>1</mn> </mrow> </mfenced> </mrow> </mfenced> </math></EquationSource> </InlineEquation> co-occurrence. At <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\tilde{t}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>t</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation>= 0.90 (the 90th percentile), the warm–dry and cold–wet co-occurrence (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\tilde{J}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>J</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation>=0.0196) is nearly double the independence (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">$\tilde{B}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>B</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation>=0.0100) expectation (a 97% increase with (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">$\tilde{R}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>R</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation>=1.969). By contrast, warm–wet and cold–dry are suppressed (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">$\tilde{J}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>J</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation>=0.0015 and 0.0030) and co-occur well below independence (an 85 and 70% decrease with <InlineEquation ID="IEq7"> <EquationSource Format="TEX">$\tilde{R}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>R</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation> = 0.151 and <InlineEquation ID="IEq8"> <EquationSource Format="TEX">$\tilde{R}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>R</mi> <mo stretchy="false">~</mo> </mover> </math></EquationSource> </InlineEquation> = 0.303, respectively). The approach yields station-specific multipliers for compound event risk and a generalizable procedure that disentangles within-series dynamics from bivariate dependence.</p>

Beyond correlation: quantifying compound hydro-climatic shocks with copulas

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2026年6月25日

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Youssef Saliba,
Alina Bărbulescu

摘要

Compound temperature–precipitation shocks shape hydro-climatic impacts in semi-arid regions but are not fully represented by linear correlation. Using monthly observations from Tulcea, Dobrogea (1965–2019), we quantify contemporaneous dependence by isolating independent and identically distributed innovation shocks and modeling their joint distribution with copulas. Temperature is filtered with a periodic autoregressive moving average of orders 2 and 1 and a periodic Generalized Autoregressive Conditional Heteroskedasticity variance model, and fitted with a Student-t marginal. For precipitation, we use a periodic autoregressive of first-order model with a Generalized Extreme Value marginal. Probability-integral transforms (PIT) yield uniform innovations. Candidate copulas are estimated by maximum likelihood, selected via Akaike/Bayesian criteria (AIC/BIC), and checked with White’s information-matrix test. The Frank copula is selected for all four compound configurations (warm–dry, cold–wet, warm–wet, cold–dry), implying a zero asymptotic tail dependence but non-trivial finite quantile $\left( {\tilde{t} \in \left( {0,1} \right)} \right)$ $(\tilde{t} \in (0, 1))$ co-occurrence. At $\tilde{t}$ $\tilde{t}$ = 0.90 (the 90th percentile), the warm–dry and cold–wet co-occurrence ( $\tilde{J}$ $\tilde{J}$ =0.0196) is nearly double the independence ( $\tilde{B}$ $\tilde{B}$ =0.0100) expectation (a 97% increase with ( $\tilde{R}$ $\tilde{R}$ =1.969). By contrast, warm–wet and cold–dry are suppressed ( $\tilde{J}$ $\tilde{J}$ =0.0015 and 0.0030) and co-occur well below independence (an 85 and 70% decrease with $\tilde{R}$ $\tilde{R}$ = 0.151 and $\tilde{R}$ $\tilde{R}$ = 0.303, respectively). The approach yields station-specific multipliers for compound event risk and a generalizable procedure that disentangles within-series dynamics from bivariate dependence.