Abstract
Monitoring of air temperature has implications in a wide range of environmental applications. Air temperature commonly measured with meteorological stations provides a high accuracy and temporal resolution for specific monitoring sites. However, in regions with highly variable topography and scare monitoring such as the case of the southern Ecuadorian Andes, these in situ data poorly describe the spatial variability of air temperature. Thus, remote sensing data has a great potential to estimate the spatial distribution of climatological variables due to the spatial continuity of the information. This research aims to estimate the spatial distribution of the monthly air temperature in the Paute river basin for the period 2014–2017, using statistical and geostatistical methods: linear regression (LR), random forest regression (RF), and regression kriging (RK), in addition to evaluate the use of altitude and other auxiliary variables (land surface temperature, latitude, and longitude). Cross-validation showed that RF performed better than LR as well as when using auxiliary variables compared to only the altitude (LR-altitude: RMSE=1.325 °C, P bias= −0.150%, r=0.775; LR-auxiliary variables: RMSE=1.265 °C, P bias=0.000% r=0.795; RF-altitude: RMSE=1.235 °C, P bias =0.200%, r=0.810; RF-auxiliary variables RMSE=1.205 °C, P bias =0.200%, r=0.820). The application of regression kriging was limited since less than 50% of the months had spatial autocorrelation in the regression model residuals. Nevertheless, in these months, regression kriging increased the estimation performance. The outcomes of this research work increase the understanding of the spatial distribution of monthly air temperature in the Paute river basin, which will improve hydrological modeling.
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Acknowledgements
This research was funded by Corporación Eléctrica del Ecuador (CELEC-Hidropaute) and Universidaddel Azuay (UDA) (Project name: “Convenio de cooperación interinstitucional para la creación de una base de datos geográfica para la gestión y análisis de información espacio-temporal para el manejo de zonas inestables y cuencas hidrográficas del complejo paute integral); and Decanato de Investigaciones from UDA (Project name “Datos abiertos de variables climáticas esenciales: reportes automatizados y actualizados a partir de fuentes de datos satelitales”). The authors would like to thank to Instituto Nacional de Eficiencia Energética y Energías Renovables (INER) and CELEC-Hidropaute for providing data from meteorological stations, to Paul Martinez from CELEC-Hidropaute, and Omar Delgado from UDA for their support in this research. This work is the results of master’s thesis in geomatics from the National University of Colombia.
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This research was funded by Corporación Eléctrica del Ecuador (CELEC-Hidropaute) and Universidad del Azuay.
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In this study, ML.O, D.B, and JE.O designed the methodology; ML.O and D.B performed the numerical experiments and analyzed the results with observations from JE.O and P.G; ML.O wrote the paper with contributions of D.B, JE.O, and P.G.
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Orellana-Samaniego, M.L., Ballari, D., Guzman, P. et al. Estimating monthly air temperature using remote sensing on a region with highly variable topography and scarce monitoring in the southern Ecuadorian Andes. Theor Appl Climatol 144, 949–966 (2021). https://doi.org/10.1007/s00704-021-03583-3
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DOI: https://doi.org/10.1007/s00704-021-03583-3