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<p>Accurate precipitation estimation with high temporal resolution is crucial to monitor and predict natural hazards in mountainous regions. While rain gauges are the reliable source of precipitation data, they lack continuous fine resolution at desired locations such as avalanche and landslide sites. In this context, temporal disaggregation approaches can be used to obtain continuous hourly precipitation time series that account for the issues observed in mountainous regions, such as, (i) filling gaps in the data, (ii) capturing fine resolution statistical properties using available nearest station record, and (iii) availing longer historical records for better hindcasting. Multiple Point statistics (MPS) approaches are known to mimic spatial patterns from the observed physical reality. This study introduces SDHPM, a temporal disaggregation approach, that investigates the possibility of MPS to search and generate complex temporal patterns. The objective is to simulate hourly precipitation time series from observed daily precipitation at multiple avalanche sites. Moreover, combinations of precipitation time series from different locations and in varying numbers are tested as covariates. The results reveal that SDHPM produces hourly precipitation ensembles of realistic time patterns over a complex and extensive mountain terrain to improve avalanche and landslide forecasting. The representation of extreme events in simulations is improved while preserving spatial and distributional characteristics by applying a Cluster-aware Beta inflation method.</p>

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Statistical disaggregation approach for generating hourly precipitation in mountainous regions preserving complex temporal patterns

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Nibedita Samal,
  • K. V. Meenakshi,
  • Akshay Singhal,
  • Sanjeev K. Jha,
  • Fabio Oriani

摘要

Accurate precipitation estimation with high temporal resolution is crucial to monitor and predict natural hazards in mountainous regions. While rain gauges are the reliable source of precipitation data, they lack continuous fine resolution at desired locations such as avalanche and landslide sites. In this context, temporal disaggregation approaches can be used to obtain continuous hourly precipitation time series that account for the issues observed in mountainous regions, such as, (i) filling gaps in the data, (ii) capturing fine resolution statistical properties using available nearest station record, and (iii) availing longer historical records for better hindcasting. Multiple Point statistics (MPS) approaches are known to mimic spatial patterns from the observed physical reality. This study introduces SDHPM, a temporal disaggregation approach, that investigates the possibility of MPS to search and generate complex temporal patterns. The objective is to simulate hourly precipitation time series from observed daily precipitation at multiple avalanche sites. Moreover, combinations of precipitation time series from different locations and in varying numbers are tested as covariates. The results reveal that SDHPM produces hourly precipitation ensembles of realistic time patterns over a complex and extensive mountain terrain to improve avalanche and landslide forecasting. The representation of extreme events in simulations is improved while preserving spatial and distributional characteristics by applying a Cluster-aware Beta inflation method.