Preprints
https://doi.org/10.5194/hess-2024-155
https://doi.org/10.5194/hess-2024-155
14 Jun 2024
 | 14 Jun 2024
Status: this preprint is currently under review for the journal HESS.

GDHPM: A Geostatistical Disaggregation approach for generating hourly Precipitation in Mountainous regions preserving complex temporal patterns

Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani

Abstract. 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 Geostatistics (MPS) approaches are known to mimic spatial patterns from the observed physical reality. This study introduces GDHPM, 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 auxiliary time series from different locations and in varying numbers are tested as covariates. The results reveal that GDHPM produces hourly precipitation ensembles of realistic time patterns over a complex and extensive mountain terrain to improve avalanche and landslide forecasting.

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Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani

Status: open (until 09 Aug 2024)

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  • RC1: 'Comment on hess-2024-155', Anonymous Referee #1, 12 Jul 2024 reply
Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani
Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani

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Short summary
A statistical approach is developed for the first time to generate hourly rainfall from daily records at multiple avalanche sites of mountainous terrain. The approach reproduces complex temporal patterns of past rainfall information in the future by using auxiliary information from nearby sites. The high temporal resolution data produced is reliable and also produces extreme rainfall patterns well. The hourly precipitation data can be used for better prediction of avalanches and landslides.