High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Abstract

This study describes the development of PISCOt (v1.2), an innovative high-spatial resolution (0.01°) daily air temperature dataset for Peru (1981-2020). The development of PISCOt involves four main steps: i) quality control; ii) gap-filling; iii) homogenisation of weather stations; and iv) spatial interpolation. The methodological framework allows the representation of the complex spatial variability of air temperature at a more accurate scale than other national and global products (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). The technical validation indicates mean absolute errors of less than 1.5 °C at climatological and daily mean scales. The new PISCOt dataset appropriately captures the temporal trends which highlights its usefulness to understand the historical variability of air temperature. For the first time, PISCOt v1.2 provides a suitable and widely applicable baseline at the local and regional level in the face of data scarcity in several regions of Peru for applications related to climate change, water balance studies, or the assessment of ecosystems, among others.

Assessment of Present and Future Water Security under Anthropogenic and Climate Changes Using WEAP Model in the Vilcanota-Urubamba Catchment, Cusco, Perú

“…The hydrologic data used in the calculation are precipitation, evapotranspiration, and streamflow. The climate data were obtained from the gridded products PISCO ("Peruvian Interpolate Data of the SENAMHI's Climatological and Hydrological Observations") [21,22] and meteorological stations. The GCMs come from the NEX-GDDP [23], consisting of 21 models for the RCP scenarios 4.5 and 8.5.…”

A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments

“…Many authors have argued that the lack of meteorological observations in the Peruvian Andes and the Amazonia is the main limitation for climate change impact studies (Andres et al, 2014; Aybar et al, 2019; Lavado Casimiro et al, 2011; Llauca, Lavado‐Casimiro, León, et al, 2021; Llauca, Lavado‐Casimiro, Montesinos, et al, 2021; Rau et al, 2018; Vegas Galdos et al, 2015; Zulkafli, 2014). To address this issue, Aybar et al (2019) and Huerta et al (2018) developed the Peruvian Interpolated data of SENAMHI's Climatological and hydrological Observations (PISCO) database, which is used in this study to force hydrologic model simulations. The PISCO database provides daily time series of precipitation, minimum and maximum temperature for the period 1981–2016, with a 0.1° horizontal resolution.…”

“…Precipitation time series in PISCOp (Aybar et al, 2019) were obtained using geostatistical and deterministic interpolation methods that include three precipitation sources: (1) the quality‐controlled and infilled national rain gauge dataset, (2) radar‐gauge merged precipitation climatologies, and (3) the Climate Hazards Group Infrared Precipitation (CHIRP) estimates. Daily time series of maximum and minimum temperature in PISCOt (Huerta et al, 2018) were obtained from: (1) observed maximum and minimum temperature data, (2) soil temperature product from the MODIS sensor (Moderate Resolution Imaging Spectroradiometer), and (3) geographic predictors (e.g., elevation, longitude, latitude and Topographic Dissection Index). The reader is referred to Huerta et al (2018) for full descriptions on the development of temperature products.…”

“…Daily time series of maximum and minimum temperature in PISCOt (Huerta et al, 2018) were obtained from: (1) observed maximum and minimum temperature data, (2) soil temperature product from the MODIS sensor (Moderate Resolution Imaging Spectroradiometer), and (3) geographic predictors (e.g., elevation, longitude, latitude and Topographic Dissection Index). The reader is referred to Huerta et al (2018) for full descriptions on the development of temperature products. The PISCO product is freely available through the IRI Data Library website (http://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/).…”

Calibración de la curva número del modelo SCS para la región de la costa norte del Perú

“…La estimación de caudales de una cuenca es fundamental para la planificación del uso de los recursos hídricos, para la planificación agrícola y urbana así como para el dimensionamiento de las obras de infraestructura física, principalmente hidráulicas [1]. Referente a este último punto, el ingeniero muchas veces para estimar la magnitud de los caudales de diseño de las obras hidráulicas de protección y de aprovechamiento está obligado a utilizar métodos indirectos de lluvia-escorrentía [2], sobre todo cuando se carece de información hidrometeorológica que permita calcular volúmenes de agua con mediciones directas.…”