Small‐scale topographic effects on precipitation distribution in San Dimas experimental forest

Burns, Joseph I.

doi:10.1029/tr034i005p00761

Cited by 43 publications

(19 citation statements)

References 9 publications

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“…Furthermore, owing to the ClausiusClapeyron effect, the decreasing density of the air results in a strong reduction of moisture available for condensation. At a certain elevation level, this effect can be expected to outbalance the increase in precipitation caused by the temperature decrease with elevation (Burns, 1953;Alpert, 1986;Roe and Baker, 2006). Following such considerations, Havlik (1969) expected such a precipitation maximum above 3500 m a.s.l.…”

Section: Discussionmentioning

confidence: 99%

Elevation dependency of mountain snow depth

Bühler²,

2014

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Abstract. Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevationsnow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation-snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

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Section: Discussionmentioning

confidence: 99%

Elevation dependency of mountain snow depth

Bühler²,

2014

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“…Previous studies have shown that precipitation patterns are more highly correlated with broad-scale topographic features than with local features (e.g. Spreen, 1947;Burns, 1953;Schermerhorn, 1967;Daly et al, 1994;Kyriakidis et al, 2001). Many of these studies and results from other PRISM modelling activities suggest that 2.5 is near the optimum resolution for modelling precipitation.…”

Section: Elevation Datamentioning

confidence: 99%

Mapping the climate of Puerto Rico, Vieques and Culebra

Daly¹,

Helmer²,

Quiñones³

2003

Intl Journal of Climatology

238

216

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Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regressionbased parameter-elevation regressions on independent slopes model (PRISM) uses spatial data sets, a knowledge base and expert interaction to generate grids of climate variables that are compatible with geographical information systems. This study applied PRISM to generate maps of mean monthly and annual precipitation and minimum and maximum temperature for the Caribbean islands of Puerto Rico, Vieques and Culebra over the 1963-95 averaging period. PRISM was run under alternative parameterizations that simulated simpler interpolation methods as well as the full PRISM model. For temperature, the standard PRISM parameterization was compared with a hypsometric method (HYPS), in which the temperature-elevation slope was assumed to be −6.5°C/km. For precipitation, the standard PRISM parameterization was compared with an inverse-distance weighting interpolation (IDW). Spatial temperature patterns were linked closely to elevation, topographic position, and coastal proximity. Both PRISM and HYPS performed well for July maximum temperature, but HYPS performed relatively poorly for January minimum temperature, due primarily to lack of a spatially varying temperature-elevation slope, vertical atmospheric layer definition, and coastal proximity guidance. Mean monthly precipitation varied significantly throughout the year, reflecting seasonally differing moisture trajectories. Spatial precipitation patterns were associated most strongly with elevation, upslope exposure to predominant moisture-bearing winds, and proximity to the ocean. IDW performed poorly compared with PRISM, due largely to the lack of elevation and moisture-availability information. Overall, the full PRISM approach resulted in greatly improved performance over simpler methods for precipitation and January minimum temperature, but only a small improvement for July maximum temperature. Comparisons of PRISM mean annual temperature and precipitation maps with previously published handdrawn maps showed similar overall patterns and magnitudes, but the PRISM maps provided much more spatial detail.

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“…The phenomenology of WDR is most thoroughly understood from its destructive effects on exterior building surfaces, for which a vast technical literature exists (Underwood and Meentemeyer, 1998;Blocken and Carmeliet, 2004). WDR complicates the accurate estimation of rainfall totals (Burns, 1953;Jackson and Aldridge, 1972), catchment runoff and the timing of floods (Horton 1919;Hamilton, 1944), and soil erosion and the resulting transport of sediment (Helming, 1999;Erpul et al, 2002). As observed by Fourcade (1889), WDR affects the azimuthal distribution of vegetation on sloping terrain, particularly in a semi-arid climate where plants are most sensitive to differences in rainfall and slope exposure.…”

Section: Wind-driven Rainfallmentioning

confidence: 99%