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<p>Water management in southeastern Iran faces persistent challenges due to its arid climate, irregular rainfall, and sparse rain gauge networks. Hydrological monitoring and planning in data-deficient areas can be effectively supported by satellite-derived and reanalysis precipitation estimates. This study applies the HBV-light model to evaluate eight satellite and reanalysis precipitation products across six watersheds in Kerman Province, aiming to identify datasets best suited for operational decision-making, socio-economic planning, and adaptive water governance in data-scarce regions. Model calibration was performed using genetic algorithms and optimization techniques. Results showed significant variation in performance. CMORPH CRT, TMPA 3B42v7, and PERSIANN CDR achieved the highest accuracy, with CMORPH CRT and PERSIANN CDR yielding NSE ≈ 0.66. In contrast, CMORPH RAW and CFSR performed poorly (NSE = − 0.36 and − 0.34). A comparative ranking table was developed to guide dataset selection for governance applications. CMORPH CRT (NSE = 0.75–0.84, PBIAS &lt; 15%) ranked first, ideal for real-time runoff estimation and water allocation in ungauged basins. TMPA 3B42v7 supported drought monitoring and agricultural planning, while CHIRPS proved useful for seasonal water budgeting. PERSIANN CDR showed moderate utility for long-term climate analysis. Lower-ranked products like TMPA RT, GPCC, and CFSR were unsuitable for direct planning but may serve in validation or emergency contexts. CMORPH CRT, despite limited use in Iranian studies, outperformed established gauge-adjusted datasets, demonstrating the potential of satellite and reanalysis products as reliable alternatives to sparse rain gauge data and strategic tools for adaptive water governance in southeastern Iran.</p>

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Socio-economic planning through satellite-based precipitation assessment for water governance in data-scarce basins: a case study from Kerman Province, Iran

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Mahin Fooladi,
  • Iman Islami,
  • Jüri Lehtsaar,
  • Imaneh Goli,
  • Hossein Azadi

摘要

Water management in southeastern Iran faces persistent challenges due to its arid climate, irregular rainfall, and sparse rain gauge networks. Hydrological monitoring and planning in data-deficient areas can be effectively supported by satellite-derived and reanalysis precipitation estimates. This study applies the HBV-light model to evaluate eight satellite and reanalysis precipitation products across six watersheds in Kerman Province, aiming to identify datasets best suited for operational decision-making, socio-economic planning, and adaptive water governance in data-scarce regions. Model calibration was performed using genetic algorithms and optimization techniques. Results showed significant variation in performance. CMORPH CRT, TMPA 3B42v7, and PERSIANN CDR achieved the highest accuracy, with CMORPH CRT and PERSIANN CDR yielding NSE ≈ 0.66. In contrast, CMORPH RAW and CFSR performed poorly (NSE = − 0.36 and − 0.34). A comparative ranking table was developed to guide dataset selection for governance applications. CMORPH CRT (NSE = 0.75–0.84, PBIAS < 15%) ranked first, ideal for real-time runoff estimation and water allocation in ungauged basins. TMPA 3B42v7 supported drought monitoring and agricultural planning, while CHIRPS proved useful for seasonal water budgeting. PERSIANN CDR showed moderate utility for long-term climate analysis. Lower-ranked products like TMPA RT, GPCC, and CFSR were unsuitable for direct planning but may serve in validation or emergency contexts. CMORPH CRT, despite limited use in Iranian studies, outperformed established gauge-adjusted datasets, demonstrating the potential of satellite and reanalysis products as reliable alternatives to sparse rain gauge data and strategic tools for adaptive water governance in southeastern Iran.