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

Abstract

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.

Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution

“…An important conclusion of this work is that installing high-resolution (hourly or less) precipitation gauges in previously ungauged locations next to the customary, manual rainmeasuring instrument, even for short periods, has tangible benefits in the estimation of long-term precipitation statistics, such as interannual variability and quantiles of annual precipitation with high return periods. This is important because accurate gauge-level precipitation estimates remain vital for the correction of remotely sensed data and in merging different precipitation data types, e.g., weather radar, and satellite (e.g., Xie and Arkin, 1996), as well as for the spatial interpolation of precipitation, especially in areas with complex topography (e.g., Masson and Frei, 2014).…”

Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru)

“…Several studies showed that kriging with external drift (KED), using altitude as an external variable, provides good results over complex terrains (e.g., Masson and Frei, 2014;Tobin et al, 2011;Ochoa et al, 2014). Block kriging with altitude as an external drift was thus chosen here as our reference interpolation method -note however that other types of kriging interpolators were tested, but a cross-validation evaluation showed KED to be the most efficient of all in our case.…”

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

“…Future developments will focus on increasing the performances in data-sparse regions, e.g., following the recommendations of Masson and Frei (2014) on the use of climatological precipitation fields for the interpolation of daily precipitation. Furthermore, the issue of wind-induced underestimation of solid precipitation will be addressed.…”

“…In this paper, we will use E-OBS as a reference dataset to evaluate seNorge2. The conclusions of the work by Masson and Frei (2014) favor the use of statistical interpolation schemes based on a two-step approach, where the background is estimated from the data, such as Kriging with external drift that is rather similar to OI. In addition, they conclude that the inclusion of a single topographic predictor may be sufficient in the interpolation, and they support "the common practice of using a climatological mean field as a background in the interpolation of daily precipitation".…”