Geostatistical Mapping of Precipitation from Rain Gauge Data Using Atmospheric and Terrain Characteristics

Kyriakidis, Phaedon C.; Kim, Jinwon; Miller, Norman L.

doi:10.1175/1520-0450(2001)040<1855:gmopfr>2.0.co;2

Cited by 112 publications

(94 citation statements)

References 30 publications

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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

236

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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Section: Elevation Datamentioning

confidence: 99%

Mapping the climate of Puerto Rico, Vieques and Culebra

Daly¹,

Helmer²,

Quiñones³

2003

Intl Journal of Climatology

236

216

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“…There are different emphases in the two extensions between robustness and local representativity of the precipitation-topography model used for interpolation. The third extension of traditional precipitation-height models is to incorporate information on atmospheric flow conditions into the interpolation; Kyriakidis et al (2001) constructed new rainfall predictors by combination of lower-atmosphere flow and moisture with local terrain height and slope. When used in kriging these dynamical predictors yielded more accurate interpolations of the seasonal mean precipitation compared to using only elevation.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

Masson

Frei

2014

Hydrol. Earth Syst. Sci.

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Abstract. Statistical models of the relationship between precipitation and topography are key elements for the spatial interpolation of rain-gauge measurements in high-mountain regions. This study investigates several extensions of the classical precipitation-height model in a direct comparison and within two popular interpolation frameworks, namely linear regression and kriging with external drift. The models studied include predictors of topographic height and slope at several spatial scales, a stratification by types of a circulation classification, and a predictor for wind-aligned topographic gradients. The benefit of the modeling components is investigated for the interpolation of seasonal mean and daily precipitation using leave-one-out cross-validation. The study domain is a north-south cross section of the European Alps (154 km × 187 km) that is inclined towards dense rain-gauge measurements (approx. 440 stations, 1971-2008).The significance of the topographic predictors was found to strongly depend on the interpolation framework. In linear regression, predictors of slope and at multiple scales reduce interpolation errors substantially. But with as many as nine predictors, the resulting interpolation still poorly replicates the across-ridge variation of climatological mean precipitation. Kriging with external drift (KED) leads to much smaller interpolation errors than linear regression, but this is achieved with a single predictor (local topographic height), whereas the incorporation of more extended predictor sets brings only marginal further improvement. Furthermore, the stratification by circulation types and the wind-aligned gradient predictor do not improve over the single predictor KED model. As for daily precipitation, interpolation accuracy improves considerably with KED and the use of a single predictor field (the distribution of seasonal mean precipitation) as compared to ordinary kriging (i.e., without any predictor). Nonetheless, information from circulation types did not improve interpolation accuracy.Our results confirm that the consideration of topography effects is important for spatial interpolation of precipitation in high-mountain regions. But a single predictor may be sufficient and taking appropriate account of the spatial autocorrelation (by kriging) can be more effective than the development of elaborate predictor sets within a regression model. Our results also question a popular practice of using linear regression for predictor selection in spatial interpolation; however they support the common practice of using a climatological mean field as a background in the interpolation of daily precipitation.

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“…The spatial interpolation techniques have been used for mapping the spatial patterns of climatic fields in several regions of the world, such as France [22], Germany [23], the UK [24], Italy [25], Mexico [26,27], Portugal [28,29], and the USA [30]. There have been a few studies conducted on the Bangladesh climate, based on the data from meteorological stations [2,21,[31][32][33][34], but so far no study has been conducted in Bangladesh on comparing the performance and prediction ability of different spatial interpolation methods to produce climate surfaces.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

Bhowmik

2012

ISRN Meteorology

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This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices' values. Both the tendency of methods' over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures-the index of agreement (d) and the coefficient of variation of prediction (ρ f )-imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.

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Geostatistical Mapping of Precipitation from Rain Gauge Data Using Atmospheric and Terrain Characteristics

Cited by 112 publications

References 30 publications

Mapping the climate of Puerto Rico, Vieques and Culebra

Mapping the climate of Puerto Rico, Vieques and Culebra

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

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