پهنه‌بندی خشک‌سالی در استان یزد با استفاده از PNPI و SIAP

Abstract
Drought is recognized as one of the most widespread, frequent, and significant natural hazards globally, and particularly in Iran. Accordingly, continuous monitoring of drought is essential as a critical tool for effective management and mitigation of its impacts. In past decades, drought analysis has predominantly been descriptive; however, contemporary approaches emphasize the use of precipitation-based indices to enable quantification, precise evaluation, and efficient monitoring of this phenomenon. The present study aims to monitor drought conditions in Yazd Province using two precipitation-related indices: the Percentage of Normal Precipitation Index (PNPI) and the Standardized Index of Annual Precipitation (SIAP). Methodologically, the research is applied in nature and employs a descriptive-analytical approach. Annual precipitation data from 11 synoptic stations across the province over 25 years (2000–2024) were utilized as the statistical population of the study. In the implementation phase, following data preparation and validation tests, the selected indices were calculated for each station within the study period. Subsequently, various interpolation methods were assessed within the ArcGIS environment, and the Inverse Distance Weighting (IDW) technique was identified as the optimal method. Using this approach, spatial zoning maps for the PNPI and SIAP indices were generated and analyzed. The analysis of the maps and corresponding data revealed that the years 2002, 2008, 2013, 2014, 2019, and 2020 experienced the most severe drought conditions across the province. In contrast, the years 2001, 2007, 2015, 2021, 2022, and 2023 were characterized by more favorable conditions, with the absence of drought in most regions. Furthermore, based on Spearman’s correlation test, a strong and direct relationship was observed between the PNPI and SIAP indices and the amount of precipitation.
 
Extended Abstract
Background and Objective
Drought is one of the most pervasive and impactful natural hazards globally, especially in arid and semi-arid regions such as Iran. Its complex and gradual nature, along with its far-reaching socio-economic and environmental consequences, underscores the need for accurate monitoring and evaluation. Over the past decades, traditional drought analyses have primarily relied on descriptive assessments. However, with advancements in data processing and geospatial technologies, the utilization of quantitative, precipitation-based indices has become essential for effective drought monitoring and early warning systems.
This study focuses on the central province of Yazd, which is highly vulnerable to drought due to its low annual precipitation and desert climate. The main objective is to monitor and evaluate drought patterns across the province using two key precipitation-based indices: the Percent of Normal Precipitation Index (PNPI) and the Standardized Annual Precipitation Index (SIAP). By applying geostatistical interpolation methods in GIS, particularly the Inverse Distance Weighting (IDW) method, this research aims to provide spatially distributed drought maps, identify temporal trends, and assess the correlation of these indices with precipitation records. The results are intended to support regional water resource management and strategic planning for climate adaptation.
 
Methodology
This applied research adopts a descriptive-analytical approach to monitor drought conditions across Yazd province over 25 years (2000–2024). Annual precipitation data from 11 synoptic stations were collected and verified for completeness and consistency. Two precipitation-based drought indices, PNPI (Percent of Normal Precipitation Index) and SIAP (Standardized Annual Precipitation Index), were calculated using Microsoft Excel. These indices quantify drought severity based on deviations from long-term precipitation averages.
To visualize and analyze the spatial distribution of drought across the province, various interpolation methods available in ArcGIS were evaluated, including Kriging, Spline, and Inverse Distance Weighting (IDW). The accuracy of these methods was assessed using Root Mean Square Error (RMSE) values from control stations. IDW was selected as the optimal interpolation technique due to its lower error rates. Using this method, zoning maps for both PNPI and SIAP were generated for each year. In addition, the Spearman rank correlation coefficient was employed in MATLAB to measure the statistical relationship between the indices and observed precipitation data across stations.
 
Findings
The spatial and temporal analysis of drought conditions in Yazd province using PNPI and SIAP indices revealed significant interannual variability in drought severity and extent. The results indicate that the years 2002, 2008, 2013, 2014, 2019, and 2020 experienced the most severe droughts, with over 90% of the province affected in some cases. In contrast, years such as 2001, 2007, 2015, 2021, 2022, and 2023 were characterized by favorable precipitation conditions and minimal drought coverage.
The spatial maps generated through the IDW method showed a high degree of consistency between PNPI and SIAP, confirming their reliability in capturing drought patterns. Moreover, the Spearman correlation coefficients between the two indices and annual precipitation were very high—often reaching values close to or equal to 1—which underscores the strong dependence of both indices on precipitation variability.
These findings confirm that PNPI and SIAP are effective tools for drought monitoring in arid regions and can be reliably used in early warning systems and resource planning efforts in central Iran.
 
Conclusion
This study demonstrates the effectiveness of PNPI and SIAP indices in monitoring drought patterns across Yazd province over 25 years. The strong correlation of both indices with precipitation data confirms their reliability in quantifying drought severity in arid and semi-arid climates. The spatial distribution maps produced using the IDW interpolation method offer valuable insights into drought-prone areas and temporal fluctuations in drought intensity.
Given the increasing frequency and severity of drought events in recent decades, especially in central Iran, the application of reliable and precipitation-based indices such as PNPI and SIAP is crucial for effective water resource management, early warning systems, and long-term climate adaptation strategies. The methodology and findings of this research provide a practical framework for policymakers and planners to anticipate drought impacts and design appropriate mitigation measures.