High Space-Time Resolution Observation of Extreme Orographic Rain Gradients in a Pacific Island Catchment

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Serval ID
serval:BIB_3FF56C61467E
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
High Space-Time Resolution Observation of Extreme Orographic Rain Gradients in a Pacific Island Catchment
Journal
Frontiers in Earth Science
Author(s)
Benoit L., Lucas M., Tseng H., Huang Y.-F., Tsang Y.-P., Nugent A. D., Giambelluca T. W., Mariethoz G.
ISSN
2296-6463
Publication state
Published
Issued date
14/01/2021
Peer-reviewed
Oui
Volume
8
Language
english
Abstract
In the vicinity of orographic barriers, interactions between mountains and prevailing winds can enhance rainfall and generate strong spatial gradients of precipitation. Orographic rainfall is still poorly quantified despite being an important driver of headwater catchment hydrology, in particular when considered at high space-time resolution. In this paper, we propose a complete framework for the observation and quantification of orographic rainfall gradients at the local scale. This framework, based on the stochastic interpolation of drop-counting rain gauge observations, provides reconstructions of local rain fields at high space-time resolution. It allows us to capture the life-cycle of individual rain cells, which typically occurs at a spatial scale of approximately 1–5 km and a temporal scale of approximately 5–15 min over our study area. In addition, the resulting rain estimates can be used to investigate how rainfall gradients develop during rain storms, and to provide better input data to drive hydrological models. The proposed framework is presented in the form of a proof-of-concept case study aimed at exploring orographic rain gradients in Mānoa Valley, on the leeward side of the Island of Oʻahu, Hawaiʻi, USA. Results show that our network of eight rain gauges captured rainfall variations over the 6 × 5 km2 study area, and that stochastic interpolation successfully leverages these in-situ data to produce rainfall maps at 200 m × 1 min resolution. Benchmarking against Kriging shows better performance of stochastic interpolation in reproducing key statistics of high-resolution rain fields, in particular rain intermittency and low intensities. This leads to an overall enhancement of rain prediction at ungauged locations.
Keywords
Rainfall mapping, Orographic rainfall gradient, Stochastic interpolation, Drop counting rain gauges, Hydrometeorology
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / P2LAP2_191395
Create date
01/03/2021 10:42
Last modification date
03/12/2022 6:48
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