Multiscale assessment of spatial precipitation variability over complex mountain terrain using a high‐resolution spatiotemporal wavelet reconstruction method

Abstract

Studying precipitation variability in the Peruvian Andes is a challenge given the high topographic variability and the scarcity of weather stations. Yet previous research has shown that a near‐linear relationship exists between precipitation and vegetation in the semiarid central Andes. We exploit this relationship by developing a new, spatially highly resolved spatiotemporal precipitation reconstruction method, using daily precipitation time series from in situ weather stations, and dekadal (10 calendar days) normalized difference vegetation index (NDVI) fields. The two data sets are combined through a wavelet decomposition method. A 4° × 4° region around Quelccaya ice cap (QIC), the world's largest tropical ice cap located in the central Peruvian Andes, was selected as study area, due to its importance for climatic, glaciologic, and paleoclimatic research. The reconstructed end product, a ~1 km2 gridded precipitation data set at dekadal temporal resolution, was validated against independent rain gauge data and compared with the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 product. This validation showed a better overall performance of our own reconstruction than the TRMM data. Additionally, a comparison of our precipitation product with snowfall measurements at the QIC summit (5670 m) shows a regionally coherent signal at the dekadal scale, suggesting that the precipitation falling at QIC is driven by regional‐ rather than local‐scale convective activity. We anticipate that this methodology and the type of data generated in this study will be useful for hydrological and glaciological studies, as well as for validation of high‐resolution downscaling products in mountain regions.

High Impact Weather Events in the Andes

“…Precipitation recycling in the tropical Andes can be 70-90% of total precipitation (Zemp et al, 2014;Bedoya-Soto et al, 2019). The strong variability of tropical Andean storms is evidenced in their fractal and multifractal properties (Poveda and Mejía, 2004;Gómez and Poveda, 2008;Hurtado and Poveda, 2009;Poveda, 2011;Mesa and Peñaranda, 2015;Posadas et al, 2015;Poveda and Salas, 2015;Yarleque et al, 2016;Duffaut-Espinosa et al, 2017).…”

Climatological and Hydrological Observations for the South American Andes: In situ Stations, Satellite, and Reanalysis Data Sets

“…To date, the development of precipitation products tends to combine the strengths of multiple and complementary data sources: gauge, satellite and reanalysis-based data, as well as model simulations in order to produce reliable precipitation estimates (Heredia et al, 2018). Combined products with local weather stations and TRMM data can also integrate the normalized difference vegetation index as shown by Yarleque et al (2016) in the Peruvian Andes.…”

Empirical–Statistical Downscaling of Austral Summer Precipitation over South America, with a Focus on the Central Peruvian Andes and the Equatorial Amazon Basin

“…The seasonal prediction of DJF precipitation over the Peruvian Andes through empirical-statistical downscaling techniques has received relatively little attention (Yarleque et al 2016;Wu et al 2018). Wu et al (2018) found that the Niño-3.4 index is not sufficient to predict precipitation over the southwestern Peruvian Andes, even though the Niño-3.4 index has a high correlation with precipitation in this region.…”