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<p>Understanding the spatio-temporal variability of extreme rainfall is critical for climate adaptation and for informed water-resource planning in Zimbabwe. This study develops a unified statistical framework to: (1) characterize annual rainfall variability and detect monotonic trends; (2) model extreme rainfall events via block maxima and peaks-over-threshold methods and quantify their long-range dependence; (3) assess the spatial autocorrelation patterns of rainfall across provinces; and (4) explore inter-provincial rainfall dynamics using a Vector Autoregressive (VAR) model. Monthly rainfall totals for ten provinces (1984–2024) were retrieved from NASA POWER at provincial centroids. Descriptive statistics reveal a pronounced east–west gradient in mean annual rainfall (390.1 mm in Manicaland; 190.8 mm in Matabeleland South) and variability (Standard deviation up to 147 mm in Manicaland). The Mann–Kendall and block-bootstrap tests indicate no significant monotonic trends (all <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{p}_{{\textbf {BBS}}}&gt;\)</EquationSource> </InlineEquation>0.16), suggesting stationary annual totals over the study period. Extreme-value analysis shows uniformly positive Generalized Extreme Value (GEV) shape parameters (for example <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{\xi }_{{\textbf {GEV}}}\varvec{=0.05--0.38}\)</EquationSource> </InlineEquation>) with anomalously large estimates in Harare and the Midlands; Generalized Pareto Distribution (GPD) fits yield moderate heavy-tails (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{\xi }_{{\textbf {GPD}}}\varvec{=0.02-0.32}\)</EquationSource> </InlineEquation>) and scale parameters of 5.6–9.9 mm. Detrended fluctuation analysis produces Hurst exponents <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\varvec{H&lt;0.5}\)</EquationSource> </InlineEquation> (0.25–0.35), indicating anti-persistence and mean-reversion in extreme-rainfall maxima. Spatial autocorrelation metrics (Global Moran’s I = 0.266, <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\varvec{p=0.018}\)</EquationSource> </InlineEquation>; Geary’s <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\varvec{C = 0.631, p=0.007}\)</EquationSource> </InlineEquation>) confirm significant clustering. Local Moran’s I identifies northeastern hot-spots and southwestern cold-spots. A VAR(2) model—selected via minimum Akaike Information Criterion/Bayesian Information Criterion (also known as the Schwarz Criterion)(AIC/BIC)—highlights significant two-month persistence in Manicaland (<InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\varvec{L}_{\varvec{2}}\varvec{=0.916}\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\varvec{p=0.033}\)</EquationSource> </InlineEquation>) and one-month memory in Matabeleland North (<InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\varvec{L}_{\varvec{1}}\varvec{=1.319}\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(\varvec{p=0.049}\)</EquationSource> </InlineEquation>). These findings reveal heterogeneous heavy-tailed behaviour, mean-reversion, and spatial clustering in Zimbabwe’s rainfall extremes, underscoring the need for region-specific risk assessments and infrastructure design tailored to local hydro-climatic regimes.</p>

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Statistical modelling of spatio-temporal rainfall trends, dependence, and extremes in Zimbabwe (1984–2024)

  • 期刊论文

  • 发表日期：

    2025年11月1日

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  • Kudakwashe Hove,
  • Philimon Nyamugure,
  • Precious Mdlongwa,
  • Simon Kamwele Awala,
  • Thambo Nyathi,
  • Timothy Dube

摘要

Understanding the spatio-temporal variability of extreme rainfall is critical for climate adaptation and for informed water-resource planning in Zimbabwe. This study develops a unified statistical framework to: (1) characterize annual rainfall variability and detect monotonic trends; (2) model extreme rainfall events via block maxima and peaks-over-threshold methods and quantify their long-range dependence; (3) assess the spatial autocorrelation patterns of rainfall across provinces; and (4) explore inter-provincial rainfall dynamics using a Vector Autoregressive (VAR) model. Monthly rainfall totals for ten provinces (1984–2024) were retrieved from NASA POWER at provincial centroids. Descriptive statistics reveal a pronounced east–west gradient in mean annual rainfall (390.1 mm in Manicaland; 190.8 mm in Matabeleland South) and variability (Standard deviation up to 147 mm in Manicaland). The Mann–Kendall and block-bootstrap tests indicate no significant monotonic trends (all \(\varvec{p}_{{\textbf {BBS}}}>\) 0.16), suggesting stationary annual totals over the study period. Extreme-value analysis shows uniformly positive Generalized Extreme Value (GEV) shape parameters (for example \(\varvec{\xi }_{{\textbf {GEV}}}\varvec{=0.05--0.38}\) ) with anomalously large estimates in Harare and the Midlands; Generalized Pareto Distribution (GPD) fits yield moderate heavy-tails ( \(\varvec{\xi }_{{\textbf {GPD}}}\varvec{=0.02-0.32}\) ) and scale parameters of 5.6–9.9 mm. Detrended fluctuation analysis produces Hurst exponents \(\varvec{H<0.5}\) (0.25–0.35), indicating anti-persistence and mean-reversion in extreme-rainfall maxima. Spatial autocorrelation metrics (Global Moran’s I = 0.266, \(\varvec{p=0.018}\) ; Geary’s \(\varvec{C = 0.631, p=0.007}\) ) confirm significant clustering. Local Moran’s I identifies northeastern hot-spots and southwestern cold-spots. A VAR(2) model—selected via minimum Akaike Information Criterion/Bayesian Information Criterion (also known as the Schwarz Criterion)(AIC/BIC)—highlights significant two-month persistence in Manicaland ( \(\varvec{L}_{\varvec{2}}\varvec{=0.916}\) , \(\varvec{p=0.033}\) ) and one-month memory in Matabeleland North ( \(\varvec{L}_{\varvec{1}}\varvec{=1.319}\) , \(\varvec{p=0.049}\) ). These findings reveal heterogeneous heavy-tailed behaviour, mean-reversion, and spatial clustering in Zimbabwe’s rainfall extremes, underscoring the need for region-specific risk assessments and infrastructure design tailored to local hydro-climatic regimes.