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.
Highlights
Unlike lowland watersheds, headwater mountain catchments do not substantially aggregate the rain signal in space and time, which makes their hydrological response sensitive to rainfall variability (Anagnostou et al, 2010; Nikolopoulos et al, 2011; Paschalis et al, 2014)
The overall pattern of rainfall accumulation observed in this study is in good agreement with the rainfall climatology (Giambelluca et al, 2013), in particular: 1) the highest rain accumulations are observed at Lyon Arboretum, 2) the gauge Ridge located on the mountain ridge records more rain (80 mm) than the gauges Valley 1–4 in the flat and urbanized area, and 3) the southern gauge Valley 4 is the driest point of the study area (18 mm)
Event scale summary statistics, one should keep in mind that rain storms are delineated directly from observations, and their definition is dependent of the observation network
Summary
Headwater mountain catchments do not substantially aggregate the rain signal in space and time, which makes their hydrological response sensitive to rainfall variability (Anagnostou et al, 2010; Nikolopoulos et al, 2011; Paschalis et al, 2014) This is because on the one hand, their small geographical area is more sensitive to spatially varying rain intensity (Sivapalan and Blöschl, 1998), and on the other, their upstream location enhances their response to rainfall variability due to the lack of hydrologically diverse sub-catchments (Mandapaka et al, 2009). This is because the topographic features responsible for rain enhancement at the local scale are not fully resolved in most numerical models (Montesarchio et al, 2014; Zhang et al, 2016), and because the physical mechanisms responsible for orographic rain enhancement are still only partly understood (Bauer et al, 2015; Bony et al, 2015)
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