TY - JOUR
T1 - Terrain sensitive climate mapping for the Arequipa Department in Peru
AU - Moraes, André Geraldo de Lima
AU - Bowling, Laura Christine
AU - Zeballos-Velarde, Carlos Renzo
AU - Daneshvar, Fariborz
AU - Watkins, Alec Hale
AU - Cherkauer, Keith Aric
N1 - Publisher Copyright:
© 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
PY - 2022
Y1 - 2022
N2 - Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30-year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS-TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30-year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets.
AB - Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30-year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS-TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30-year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets.
KW - climatology
KW - interpolation
KW - precipitation
KW - temperature
KW - thin plate splines
UR - http://www.scopus.com/inward/record.url?scp=85131299830&partnerID=8YFLogxK
U2 - 10.1002/joc.7730
DO - 10.1002/joc.7730
M3 - Artículo
AN - SCOPUS:85131299830
JO - International Journal of Climatology
JF - International Journal of Climatology
SN - 0899-8418
ER -