Integrated Drought Monitoring in the Minab Esteghlal Dam Basin through Copula-Based Modeling of the Interdependence among Precipitation, Soil Moisture, and Surface Runoff

Document Type : Research Paper

Authors

1 PhD student, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran.

2 Associate Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran.

3 Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran.

4 Assistant Professor, Department of Statistics, Faculty of Basic Sciences, University of Hormozgan, Bandar Abbas, Iran.

5 Assistant Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran.

10.29252/aridbiom.2026.4152

Abstract

Drought is one of the most complex and costly natural hazards, particularly in arid and semi‑arid regions, affecting different components of the hydrological cycle such as precipitation, runoff, and soil moisture. In this study, a comprehensive multivariate drought index based on copula functions, named the Joint Drought Hydro‑Meteorological Index (JDHMI), was developed to monitor meteorological, hydrological, and agricultural droughts simultaneously in the Minab Independence Dam watershed in southern Iran. Monthly precipitation data from 15 rain gauge stations, streamflow data from the Berentin hydrometric station, and satellite‑based soil moisture data from the GLDAS dataset during the period 1993–2023 were used. First, the univariate drought indices including SPI‑12, SRI‑12, and SMDI‑12 were calculated. Then, the dependence structure among these variables was modeled using copula functions. The results of marginal distribution fitting indicated that the logistic distribution provided the best fit for precipitation and runoff with AIC values of 630.94 and 563.36, respectively, while the normal distribution was selected for soil moisture with an AIC value of 636.07. Copula analysis showed that the Frank copula performed best in modeling dependence. The Kendall’s tau coefficients were estimated as 0.441 for SPI12–SRI12 and 0.567 for SRI12–SMDI12, indicating moderate to strong dependence between these variables. The developed multivariate index demonstrated higher capability in identifying severe and persistent drought events. The maximum drought intensity, duration, and magnitude obtained from the JDHMI were 3.35, 10 months, and 21.4, respectively. Moreover, the correlation of JDHMI with SPI12 and SRI12 was 0.81 and 0.71, respectively, showing strong agreement with conventional indices. Principal component analysis also revealed that the first component explained about 92.06% of the total variance. Overall, the proposed copula‑based index provides a more comprehensive representation of drought conditions by integrating precipitation, runoff, and soil moisture information, and can serve as an effective tool for drought monitoring and water resources management in arid regions.

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References

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Journal of Arid Biome
Volume 15, Issue 2
September 2025
Pages 57-76

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