Saudi Arabia is shaped by a hydroclimatic gradient, ranging from the hyper-arid Rub’ al-Khali desert to the semi-arid mountains in the southwest, with direct implications for runoff generation, groundwater recharge, and drought risk. This research applies climate network analysis to quantify spatial coherence and identify transition belts in the seasonal precipitation cycle across Saudi Arabia. Monthly precipitation climatology (1970–2000) from WorldClim v2.1 at 2.5 arc-minute resolution was extracted for 98,719 land grid cells. These cells were assigned to three macro-zones based on annual precipitation: hyper-arid (< 75 mm), arid (75–150 mm), and semi-arid (≥ 150 mm). A stratified sampling scheme selected 7,148 representative nodes while preserving the distribution of annual rainfall. Within each zone, we constructed climate networks by linking nodes with highly similar seasonal cycles (Pearson correlation threshold, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{r}_{thr}=0.75-0.85\)</EquationSource> </InlineEquation>), computed standard network metrics (degree centrality and betweenness centrality), and identified communities via a modularity-based algorithm. The network formulation adds a structural view of connectivity, distinguishing regime cores (high degree) from bridge regions (high betweenness) that link otherwise distinct seasonal behaviors. At the reference threshold (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{r}_{thr}=0.80\)</EquationSource> </InlineEquation>), the arid interior zone shows the highest degree of coherence, with many nodes ranging from 0.4 to 0.6, indicating strong coherence in the seasonal cycle. High betweenness nodes are concentrated along transition belts and coastal and escarpment margins, indicating locations that connect distinct seasonal regimes. Community detection reveals a stable regime structure (two communities in Zones 1 and 3 and three communities in Zone 2), aligned with topographic and coastal-escarpment settings. Across thresholds from 0.75 to 0.85, the number of communities remains unchanged while network density decreases, supporting the robustness of the identified regime structure. The network representation highlights regime cores and bridges regions through seasonal cycle connectivity, in contrast to traditional precipitation summaries that treat locations independently. This offers an interpretable foundation for regional hydrologic analysis and improves preparedness for drought and flood. Graphical Abstract Graphical abstract descriptions: This visual summary serves as a pivotal entry point into the research, offering a concise overview of the study's core findings and methodologies. The upper left panel represents the data, displaying precipitation zones for Saudi Arabia derived from WorldClim v2.1 monthly climatology (1970-2000), which delineate hyper-arid, arid, and wet regimes. The lower left panel illustrates the analysis and sampling design, in which stratified sampling selects representative grid cells from the full climatological field while preserving spatial gradients. The lower right panel presents the model output for Zone 2 (the arid interior), mapping degree centrality in a correlation-based climate network, where nodes represent sampled grid cells and links encode similarity in monthly rainfall. High degree values highlight coherent rainfall cores with strongly synchronized seasonal cycles. The upper right panel shows the results for Zone 2, where community detection identifies three data-driven rainfall communities that capture the dominant seasonal regimes and delineate transition areas. Together, these four panels demonstrate that climate network analysis can condense complex hydroclimatic variability into a small number of connected regimes and climatic bridge zones that are directly relevant to drought and flood risk assessment and the design of monitoring and decision-support systems.

A Climate-Network Framework For Seasonal Precipitation Regime Mapping In Arid and Semi-Arid Regions: Application to Saudi Arabia

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发表日期：

2026年3月17日

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Yazeed Alabbad,
Ali Alnahit,
Saleh Alhathloul

摘要

Saudi Arabia is shaped by a hydroclimatic gradient, ranging from the hyper-arid Rub’ al-Khali desert to the semi-arid mountains in the southwest, with direct implications for runoff generation, groundwater recharge, and drought risk. This research applies climate network analysis to quantify spatial coherence and identify transition belts in the seasonal precipitation cycle across Saudi Arabia. Monthly precipitation climatology (1970–2000) from WorldClim v2.1 at 2.5 arc-minute resolution was extracted for 98,719 land grid cells. These cells were assigned to three macro-zones based on annual precipitation: hyper-arid (< 75 mm), arid (75–150 mm), and semi-arid (≥ 150 mm). A stratified sampling scheme selected 7,148 representative nodes while preserving the distribution of annual rainfall. Within each zone, we constructed climate networks by linking nodes with highly similar seasonal cycles (Pearson correlation threshold, \(\:{r}_{thr}=0.75-0.85\) ), computed standard network metrics (degree centrality and betweenness centrality), and identified communities via a modularity-based algorithm. The network formulation adds a structural view of connectivity, distinguishing regime cores (high degree) from bridge regions (high betweenness) that link otherwise distinct seasonal behaviors. At the reference threshold ( \(\:{r}_{thr}=0.80\) ), the arid interior zone shows the highest degree of coherence, with many nodes ranging from 0.4 to 0.6, indicating strong coherence in the seasonal cycle. High betweenness nodes are concentrated along transition belts and coastal and escarpment margins, indicating locations that connect distinct seasonal regimes. Community detection reveals a stable regime structure (two communities in Zones 1 and 3 and three communities in Zone 2), aligned with topographic and coastal-escarpment settings. Across thresholds from 0.75 to 0.85, the number of communities remains unchanged while network density decreases, supporting the robustness of the identified regime structure. The network representation highlights regime cores and bridges regions through seasonal cycle connectivity, in contrast to traditional precipitation summaries that treat locations independently. This offers an interpretable foundation for regional hydrologic analysis and improves preparedness for drought and flood.

Graphical Abstract

Graphical abstract descriptions: This visual summary serves as a pivotal entry point into the research, offering a concise overview of the study's core findings and methodologies. The upper left panel represents the data, displaying precipitation zones for Saudi Arabia derived from WorldClim v2.1 monthly climatology (1970-2000), which delineate hyper-arid, arid, and wet regimes. The lower left panel illustrates the analysis and sampling design, in which stratified sampling selects representative grid cells from the full climatological field while preserving spatial gradients. The lower right panel presents the model output for Zone 2 (the arid interior), mapping degree centrality in a correlation-based climate network, where nodes represent sampled grid cells and links encode similarity in monthly rainfall. High degree values highlight coherent rainfall cores with strongly synchronized seasonal cycles. The upper right panel shows the results for Zone 2, where community detection identifies three data-driven rainfall communities that capture the dominant seasonal regimes and delineate transition areas. Together, these four panels demonstrate that climate network analysis can condense complex hydroclimatic variability into a small number of connected regimes and climatic bridge zones that are directly relevant to drought and flood risk assessment and the design of monitoring and decision-support systems.