Journal: Region - Water Conservancy DOI: 10.32629/rwc.v9i2.5402
Abstract
Context: Precipitation and temperature estimates for the Sonora River basin in Mexico are analyzed using raster data from Chirps and WorldClim. These platforms can compensate for the lack of data due to limited or nonexistent instrumentation, providing information for territorial planning and decision-making.
Knowledge gap: Despite the information generated from remote sensors that facilitate the interpolation and estimation of climatic variables in areas without direct measurements, doubts persist about the accuracy of interpolated raster data compared to climatological records obtained on land, especially in areas of low station density and geographical diversity.
Purpose: The main objective is to evaluate the accuracy of Chirps' precipitation estimates and WorldClim's maximum and minimum temperature estimates, using monthly records from 19 climatological stations in the Sonora River basin during the period 1981-2013.
Methodology: Data were collected from Chirps and WorldClim and compared with records from weather stations using the Absolute Value of Relative Error (PBIAS) and the Ratio of Standard Deviation (RSR). R code routines were used.
Results and conclusions: The results show that the Chirps and WorldClim estimates have a "Good" to "Very Good" fit. However, cases of underestimation and overestimation were identified, mainly in temperatures. Temperature estimates showed a high degree of fit, while precipitation estimates were more varied. Raster data platforms are reliable for climate and planning studies in the region.
Keywords
climate; interpolation; low instrumentation; climatological stations; Rasters
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[1] Bai, L., Chen, Z., Xu, J. & Li, W. (2016). Multi-scale response of runoff to climate fluctuation in the headwater region of Kaidu River in Xinjiang of China. Theoretical and Applied Climatology, 125(3–4), 703-712. https://doi.org/10.1007/s00704- 015-1539-2
[2] Bai, L., Shi, C., Li, L., Yang, Y. & Wu, J. (2018). Accuracy of CHIRPS satellite-rainfall products over Mainland China. Remote Sensing, 10(3), 362. https:// doi.org/10.3390/rs10030362
[3] Belay, A. S., Yenehun, A., Nigate, F., Tilahun, S. A., Dessie, M., Moges, M. M., Chen, M., Fentie, D., Adgo, E., Nyssen, J. & Walraevens, K. (2024). Estimation of spatially distributed groundwater recharge in data-scarce regions. Journal of Hydrology: Regional Studies, 56, 102072. https://doi. org/10.1016/j.ejrh.2024.102072
[4] Dinku, T. (2019). Challenges with availability and quality of climate data in Africa. Extreme hydrology and climate variability (pp71-80). Elsevier. https://doi. org/10.1016/B978-0-12-815998-9.00007-5
[5] Fick, S. E. & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/ joc.5086
[6] Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A. & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitor ing extremes. Scientific Data, 2(1), Article 150066. https://doi.org/10.1038/sda- ta.2015.66
[7] GDAL/OGR contributors. (2024). GDAL/ OGR Geospatial Data Abstraction software Library. Zenodo. https://doi. org/10.5281/zenodo.5884351
[8] Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., ... Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. https://doi. org/10.1038/s41586-020-2649-2
[9] Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. (2013). A trend-preserving bias correction – the ISI-MIP approach. Earth System Dynamics, 4(2), 219-236. https://doi.org/10.5194/ esd-4-219-2013
[10] Hernández-Vásquez, C. C., Ibáñez-Castillo, L. A., Gómez-Díaz, J. D. & Artea- ga-Ramírez, R. (2020). Analysis of mete- orological droughts in the Sonora river basin, Mexico. Atmósfera. https://doi. org/10.20937/ATM.52954
[11] Herrera Oliva, C. S., Campos Gaytán, J. R. y Carrillo González, F. M. (2017). Estimación de datos faltantes de precipitación por el método de regresión lineal: Caso de estudio Cuenca Guadalupe, Baja California, México. Investigación y Ciencia de la Universidad Autónoma de Aguascalientes, 71, 34-44. https://doi.org/10.33064/ iycuaa201771598
[12] Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965-1978. https://doi.org/10.1002/joc.1276
[13] Infante, S. y Zárate, G. (2012). Métodos estadísticos: Un enfoque interdisciplinario (2.ª ed.). La Gaya Ciencia.
[14] Katsanos, D., Retalis, A. & Michaelides, S. (2016). Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmospheric Research, 169, 459-464.
https://doi. org/10.1016/j.atmosres.2015.05.015
[15] Kyrgyzbay, K., Kakimzhanov, Y. & Sagin, J. (2023). Climate data verification for assessing climate change in Almaty region of the Republic of Kazakhstan. Climate Services, 32, 100423. https://doi. org/10.1016/j.cliser.2023.100423
[16] López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J. & Díaz-García, J. A. (2022). Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America. Physics and Chemistry of the Earth, 127, 103184. https://doi.org/10.1016/j. pce.2022.103184
[17] Méndez, R. (2016). Productos de precipitación satelital de alta resolución espacialy temporal en las zonas de topograf ía compleja [Tesis de maestría, Pontificia Universidad Católica de Chile].
[18] Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. https://doi. org/10.13031/2013.23153
[19] Organización Meteorológica Mundial (OMM). (2018). Guía de prácticas clima- tológicas (OMM-N° 100).
[20] Pandas Development Team. (2020). pandas-dev/pandas: Pandas [Versión 1.0]. Zenodo. https://doi.org/10.5281/zeno- do.3509134
[21] Paredes Trejo, F. J., Álvarez Barbosa, H., Peñaloza-Murillo, M. A., Moreno, M. A. & Farias, A. (2016). Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmósfera, 29(4), 323-342. https://doi.org/10.20937/ ATM.2016.29.04.04
[22] Paredes Trejo, F., Alves Barbosa, H., Venkata Lakshmi Kumar, T., Kumar Thakur, M. & de Oliveira Buriti, C. (2021). Assessment of the CHIRPS-based satellite precipitation estimates. In Inland Waters - Dynamics and Ecology. IntechOpen. https:// doi.org/10.5772/intechopen.91472
[23] Peng, F., Zhao, S., Chen, C., Cong, D., Wang Y. & Ouyang, H. (2020). Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China. Atmospheric Research, 236, 104814. https://doi. org/10.1016/j.atmosres.2019.104814
[24] Perrin, G., Rapinel, S., Hubert-Moy, L. & Bioret, F. (2020). Bioclimatic dataset of Metropolitan France under current conditions derived from the WorldClim model. Data in Brief, 31, 105815.
https:// doi.org/10.1016/j.dib.2020.105815
[25] Poggio, L., Simonetti, E. & Gimona, A. (2018). Enhancing the WorldClim dataset for national and regional applications. Science of The Total Environment, 625, 1628-1643. https://doi.org/10.1016/j.sci- totenv.2017.12.258
[26] QGIS Development Team. (2023). QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.org
[27] R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/
[28] Rivera, J. A., Hinrichs, S. & Marianetti, G. (2019). Using CHIRPS dataset to assess wet and dry conditions along the semiarid Central-Western Argentina. Advances in Meteorology, 8413964. https://doi. org/10.1155/2019/8413964
[29] Romero, D. & Alfaro, E. J. (2024). Spatiotemporal variability of the rainy season in the Yucatan Peninsula. International Journal of Climatology, 44(8), 2561-2574. https://doi.org/10.1002/joc.8468
[30] Romero-Hernández, C. M., Avila-Diaz, A., Quesada, B., Medeiros, F., Cerón, W. L., Guzman-Escalante, J., Ocampo-Maru- landa, C., Rodrigues Torres, R. & Zuluaga, C. F. (2024). Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin. Journal of South American Earth Sciences, 140, 104898. https://doi. org/10.1016/j.jsames.2024.104898
[31] Shen, Z., Yong, B., Gourley, J. J., Qi, W., Lu, D., Liu, J., Ren, L., Hong, Y. & Zhang, J. (2020). Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). Journal of Hydrology, 591, 125284. https:// doi.org/10.1016/j.jhydrol.2020.125284
[32] Shrestha, N. K., Qamer, F. M., Pedreros, D., Murthy, M. S. R., Wahid, S. M. & Shrestha, M. (2017). Evaluating the accuracy of Climate Hazard Group (CHG) satellite rainfall estimates for precipitation based drought monitoring in Koshi Basin, Nepal. Journal of Hydrology: Regional Studies, 13, 138-151. https://doi.org/10.1016/j. ejrh.2017.08.004
[33] Shumilo, L. & Skakun, S. (2024). Optical flow of temperature reveals climate change patterns for agriculture and forestry. Remote Sensing Applications: Society and Environment, 34, 101198. https:// doi.org/10.1016/j.rsase.2024.101198
[34] SMN. (2019). Sistema de Información Climática Computarizada CLICOM. Servicio Meteorológico Nacional. https://cu- capa-clicom.cicese.mx/malla/index.php
[35] Tikuye, B. G., Ray, R. L., Manjunatha, B., Tefera, G. W. & Gurau, S. (2024). Drought monitoring using the Climate Hazards InfraRed Precipitation with Stations (CHIRPS) in Ethiopia. Natural Hazards Research, 5 (2), 348-362. https://doi. org/10.1016/j.nhres.2024.12.002
[36] Van Rossum, G. & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace.
https:// dl.acm.org/doi/book/10.5555/1593511
[37] Wickham, H. (2016). ggplot2: Elegant graph- ics for data analysis. Springer-Verlag.
https://ggplot2.tidyverse.org
[38] Zakeri, F. & Mariethoz, G. (2024). Synthesizing long-term satellite imagery consistent with climate data: Application to daily snow cover. Remote Sensing of Environment, 300, 113877. https://doi. org/10.1016/j.rse.2023.113877
[2] Bai, L., Shi, C., Li, L., Yang, Y. & Wu, J. (2018). Accuracy of CHIRPS satellite-rainfall products over Mainland China. Remote Sensing, 10(3), 362. https:// doi.org/10.3390/rs10030362
[3] Belay, A. S., Yenehun, A., Nigate, F., Tilahun, S. A., Dessie, M., Moges, M. M., Chen, M., Fentie, D., Adgo, E., Nyssen, J. & Walraevens, K. (2024). Estimation of spatially distributed groundwater recharge in data-scarce regions. Journal of Hydrology: Regional Studies, 56, 102072. https://doi. org/10.1016/j.ejrh.2024.102072
[4] Dinku, T. (2019). Challenges with availability and quality of climate data in Africa. Extreme hydrology and climate variability (pp71-80). Elsevier. https://doi. org/10.1016/B978-0-12-815998-9.00007-5
[5] Fick, S. E. & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/ joc.5086
[6] Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A. & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitor ing extremes. Scientific Data, 2(1), Article 150066. https://doi.org/10.1038/sda- ta.2015.66
[7] GDAL/OGR contributors. (2024). GDAL/ OGR Geospatial Data Abstraction software Library. Zenodo. https://doi. org/10.5281/zenodo.5884351
[8] Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., ... Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. https://doi. org/10.1038/s41586-020-2649-2
[9] Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. (2013). A trend-preserving bias correction – the ISI-MIP approach. Earth System Dynamics, 4(2), 219-236. https://doi.org/10.5194/ esd-4-219-2013
[10] Hernández-Vásquez, C. C., Ibáñez-Castillo, L. A., Gómez-Díaz, J. D. & Artea- ga-Ramírez, R. (2020). Analysis of mete- orological droughts in the Sonora river basin, Mexico. Atmósfera. https://doi. org/10.20937/ATM.52954
[11] Herrera Oliva, C. S., Campos Gaytán, J. R. y Carrillo González, F. M. (2017). Estimación de datos faltantes de precipitación por el método de regresión lineal: Caso de estudio Cuenca Guadalupe, Baja California, México. Investigación y Ciencia de la Universidad Autónoma de Aguascalientes, 71, 34-44. https://doi.org/10.33064/ iycuaa201771598
[12] Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965-1978. https://doi.org/10.1002/joc.1276
[13] Infante, S. y Zárate, G. (2012). Métodos estadísticos: Un enfoque interdisciplinario (2.ª ed.). La Gaya Ciencia.
[14] Katsanos, D., Retalis, A. & Michaelides, S. (2016). Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmospheric Research, 169, 459-464.
https://doi. org/10.1016/j.atmosres.2015.05.015
[15] Kyrgyzbay, K., Kakimzhanov, Y. & Sagin, J. (2023). Climate data verification for assessing climate change in Almaty region of the Republic of Kazakhstan. Climate Services, 32, 100423. https://doi. org/10.1016/j.cliser.2023.100423
[16] López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J. & Díaz-García, J. A. (2022). Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America. Physics and Chemistry of the Earth, 127, 103184. https://doi.org/10.1016/j. pce.2022.103184
[17] Méndez, R. (2016). Productos de precipitación satelital de alta resolución espacialy temporal en las zonas de topograf ía compleja [Tesis de maestría, Pontificia Universidad Católica de Chile].
[18] Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. https://doi. org/10.13031/2013.23153
[19] Organización Meteorológica Mundial (OMM). (2018). Guía de prácticas clima- tológicas (OMM-N° 100).
[20] Pandas Development Team. (2020). pandas-dev/pandas: Pandas [Versión 1.0]. Zenodo. https://doi.org/10.5281/zeno- do.3509134
[21] Paredes Trejo, F. J., Álvarez Barbosa, H., Peñaloza-Murillo, M. A., Moreno, M. A. & Farias, A. (2016). Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmósfera, 29(4), 323-342. https://doi.org/10.20937/ ATM.2016.29.04.04
[22] Paredes Trejo, F., Alves Barbosa, H., Venkata Lakshmi Kumar, T., Kumar Thakur, M. & de Oliveira Buriti, C. (2021). Assessment of the CHIRPS-based satellite precipitation estimates. In Inland Waters - Dynamics and Ecology. IntechOpen. https:// doi.org/10.5772/intechopen.91472
[23] Peng, F., Zhao, S., Chen, C., Cong, D., Wang Y. & Ouyang, H. (2020). Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China. Atmospheric Research, 236, 104814. https://doi. org/10.1016/j.atmosres.2019.104814
[24] Perrin, G., Rapinel, S., Hubert-Moy, L. & Bioret, F. (2020). Bioclimatic dataset of Metropolitan France under current conditions derived from the WorldClim model. Data in Brief, 31, 105815.
https:// doi.org/10.1016/j.dib.2020.105815
[25] Poggio, L., Simonetti, E. & Gimona, A. (2018). Enhancing the WorldClim dataset for national and regional applications. Science of The Total Environment, 625, 1628-1643. https://doi.org/10.1016/j.sci- totenv.2017.12.258
[26] QGIS Development Team. (2023). QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.org
[27] R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/
[28] Rivera, J. A., Hinrichs, S. & Marianetti, G. (2019). Using CHIRPS dataset to assess wet and dry conditions along the semiarid Central-Western Argentina. Advances in Meteorology, 8413964. https://doi. org/10.1155/2019/8413964
[29] Romero, D. & Alfaro, E. J. (2024). Spatiotemporal variability of the rainy season in the Yucatan Peninsula. International Journal of Climatology, 44(8), 2561-2574. https://doi.org/10.1002/joc.8468
[30] Romero-Hernández, C. M., Avila-Diaz, A., Quesada, B., Medeiros, F., Cerón, W. L., Guzman-Escalante, J., Ocampo-Maru- landa, C., Rodrigues Torres, R. & Zuluaga, C. F. (2024). Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin. Journal of South American Earth Sciences, 140, 104898. https://doi. org/10.1016/j.jsames.2024.104898
[31] Shen, Z., Yong, B., Gourley, J. J., Qi, W., Lu, D., Liu, J., Ren, L., Hong, Y. & Zhang, J. (2020). Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). Journal of Hydrology, 591, 125284. https:// doi.org/10.1016/j.jhydrol.2020.125284
[32] Shrestha, N. K., Qamer, F. M., Pedreros, D., Murthy, M. S. R., Wahid, S. M. & Shrestha, M. (2017). Evaluating the accuracy of Climate Hazard Group (CHG) satellite rainfall estimates for precipitation based drought monitoring in Koshi Basin, Nepal. Journal of Hydrology: Regional Studies, 13, 138-151. https://doi.org/10.1016/j. ejrh.2017.08.004
[33] Shumilo, L. & Skakun, S. (2024). Optical flow of temperature reveals climate change patterns for agriculture and forestry. Remote Sensing Applications: Society and Environment, 34, 101198. https:// doi.org/10.1016/j.rsase.2024.101198
[34] SMN. (2019). Sistema de Información Climática Computarizada CLICOM. Servicio Meteorológico Nacional. https://cu- capa-clicom.cicese.mx/malla/index.php
[35] Tikuye, B. G., Ray, R. L., Manjunatha, B., Tefera, G. W. & Gurau, S. (2024). Drought monitoring using the Climate Hazards InfraRed Precipitation with Stations (CHIRPS) in Ethiopia. Natural Hazards Research, 5 (2), 348-362. https://doi. org/10.1016/j.nhres.2024.12.002
[36] Van Rossum, G. & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace.
https:// dl.acm.org/doi/book/10.5555/1593511
[37] Wickham, H. (2016). ggplot2: Elegant graph- ics for data analysis. Springer-Verlag.
https://ggplot2.tidyverse.org
[38] Zakeri, F. & Mariethoz, G. (2024). Synthesizing long-term satellite imagery consistent with climate data: Application to daily snow cover. Remote Sensing of Environment, 300, 113877. https://doi. org/10.1016/j.rse.2023.113877
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