Vol. 11 Núm. 4 (2022): Revista de Investigaciones
Artí­culos Originales

MODELACION HIDROLÓGICA CON PRECIPITACIONES OBTENIDAS POR SATÉLITE EN LA CUENCA DEL RÍO RAMIS PERÚ

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  • Edwin Llanque Chayña
Edwin Llanque Chayña
Universidad Nacional del Altiplano Puno
"Revista de Investigaciones"

Publicado 2022-12-30

Palabras clave

  • Cuenca del río Ramis, modelización hidrológica, productos de precipitación satelital, PERSIANN-CDR, TRMM-3B42.

Derechos de autor 2022 Edwin Llanque Chayña

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

Cómo citar

Llanque Chayña, E. (2022). MODELACION HIDROLÓGICA CON PRECIPITACIONES OBTENIDAS POR SATÉLITE EN LA CUENCA DEL RÍO RAMIS PERÚ. Revista De Investigaciones, 11(4), 214-226. https://doi.org/10.26788/ri.v11i4.3918

Resumen

En los últimos años, muchos investigadores han orientado sus estudios a aprovechar los beneficios que se pueden obtener a través de las Estimaciones de Precipitación Basadas en Satélite (EPB), en escalas de tiempo diarias, mensuales y anuales. El uso de EPBs es una de las alternativas para solucionar el problema de las cuencas con poco o ningún instrumento. El objetivo de la investigación es evaluar estimaciones de precipitación basadas en satélites, utilizando modelación hidrológica en la cuenca del río Ramis, Perú. La evaluación se llevó a cabo durante 16 años (2003 - 2019), utilizando EPBs, Tropical Rainfall Measurement Mission (TRMM-3B42) y Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR). Los resultados de la comparación con estaciones meteorológicas indican que PERSIANN-CDR es mejor que TRMM-3B42 con r = 0,50 y 0,38 respectivamente. Sin embargo, el uso de las EPBS como datos de entrada en el modelo hidrológico, Herramienta de Evaluación de Suelos y Aguas (SWAT), los resultados obtenidos calibrados para TRMM-3B42 son insatisfactorios, con r = 0,77, NSE = -0,24 y el porcentaje de sesgo PBIAS = 56,50 %, para PERSIANN-CDR con r = 0,63, NSE = -0,01 y PBIAS = 62,30 %. Obteniendo un buen resultado r = 0,86 y NSE = 0,73 al utilizar las medidas de las estaciones meteorológicas. La evaluación de los datos de entrada en el modelo hidrológico muestra la magnitud del error de los EPBs, cuyos datos de entrada deben ser corregidos antes de ser utilizados

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Referencias

  1. Admas, B. F., Gashaw, T., Adem, A. A., Worqlul, A. W., Dile, Y. T., & Molla, E. (2022). Identification of soil erosion hot-spot areas for prioritization of conservation measures using the SWAT model in Ribb watershed, Ethiopia. Resources, Environment and Sustainability, 8(April), 100059. https://doi.org/10.1016/j.resenv.2022.100059
  2. Ancco, C., & Tancayllo, E. F. (2019). Estudio Hidrológico de la Unidad Hidrográfica del Río Ramis Memoria Final Estudio Hidrológico de la Unidad. Autoridad Nacional Del Agua., 01. https://repositorio.ana.gob.pe/handle/20.500.12543/4716?show=full
  3. Avand, M., Kuriqi, A., Khazaei, M., & Ghorbanzadeh, O. (2022). DEM resolution effects on machine learning performance for flood probability mapping. Journal of Hydro-Environment Research, 40(June 2021), 1–16. https://doi.org/10.1016/j.jher.2021.10.002
  4. Charles, T. da S., Lopes, T. R., Duarte, N. S., Nascimento, J. G., Ricardo, H. de C., & Pacheco, A. B. (2022). Estimating average annual rainfall by ordinary kriging and TRMM precipitation products in midwestern Brazil - ScienceDirect. Journal of South American Earth Sciences. https://doi.org/https://doi.org/10.1016/j.jsames.2022.103937.
  5. Ding, B., Liu, H., Li, Y., Zhang, X., Feng, P., Liu, D. L., Marek, G. W., Ale, S., Brauer, D. K., Srinivasan, R., & Chen, Y. (2022). Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth. Environmental Modelling and Software, 156, 105492. https://doi.org/10.1016/j.envsoft.2022.105492
  6. Eini, M. R., Rahmati, A., & Piniewski, M. (2022). Hydrological application and accuracy evaluation of PERSIANN satellite-based precipitation estimates over a humid continental climate catchment. Journal of Hydrology: Regional Studies, 41(April). https://doi.org/10.1016/j.ejrh.2022.101109
  7. Gebregiorgis, A. S., & Hossain, F. (2013). Understanding the dependence of satellite rainfall uncertainty on topography and climate for hydrologic model simulation. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 704–718. https://doi.org/10.1109/TGRS.2012.2196282
  8. Guijarro, J. A. (2018). Homogeneización de series climáticas con Climatol. Agencia Estatal de Meteorología (AEMET), D.T. En Islas Baleares, España, 1, 22. http://www.climatol.eu/homog_climatol-en.pdf
  9. Healy, R. W., & Essaid, H. I. (2012). VS2DI: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1249–1260. https://doi.org/10.13031/2013.42256
  10. Huang, W. R., Liu, P. Y., & Hsu, J. (2021). Multiple timescale assessment of wet season precipitation estimation over Taiwan using the PERSIANN family products. International Journal of Applied Earth Observation and Geoinformation, 103(88), 102521. https://doi.org/10.1016/j.jag.2021.102521
  11. Ibrahim, M., Wisser, D., Ali, A., Diekkrüger, B., Seidou, O., Mariko, A., & Afouda, A. (2017). Water balance analysis over the Niger Inland Delta-Mali: Spatio-temporal dynamics of the flooded area and water losses. Hydrology, 4(3). https://doi.org/10.3390/hydrology4030040
  12. Jiang, D., Ao, C., Bailey, R. T., Zeng, W., & Huang, J. (2022). Simulation of water and salt transport in the Kaidu River Irrigation District using the modified SWAT-Salt. Agricultural Water Management, 272(July), 107845. https://doi.org/10.1016/j.agwat.2022.107845
  13. Jimeno-Sáez, P., Martínez-España, R., Casalí, J., Pérez-Sánchez, J., & Senent-Aparicio, J. (2022). A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain. Catena, 212. https://doi.org/10.1016/j.catena.2021.105953
  14. Keikhosravi Kiany, M. S., Masoodian, S. A., Balling, R. C., & Montazeri, M. (2020). Evaluation of the TRMM 3B42 product for extreme precipitation analysis over southwestern Iran. Advances in Space Research, 66(9), 2094–2112. https://doi.org/10.1016/j.asr.2020.07.036
  15. Lakew, H. B., Moges, S. A., & Asfaw, D. H. (2017). Hydrological evaluation of satellite and reanalysis precipitation products in the Upper Blue Nile basin: A case study of Gilgel Abbay. Hydrology, 4(3). https://doi.org/10.3390/hydrology4030039
  16. Liang, D., Zuo, Y., Huang, L., Zhao, J., Teng, L., & Yang, F. (2015). Evaluation of the consistency of MODIS land cover product (MCD12Q1) based on Chinese 30 m GlobeLand30 datasets: A case study in Anhui Province, China. ISPRS International Journal of Geo-Information, 4(4), 2519–2541. https://doi.org/10.3390/ijgi4042519
  17. Lin, Q., Peng, T., Wu, Z., Guo, J., Chang, W., & Xu, Z. (2022). Performance evaluation, error decomposition and Tree-based Machine Learning error correction of GPM IMERG and TRMM 3B42 products in the Three Gorges Reservoir Area. Atmospheric Research, 268(December 2021), 105988. https://doi.org/10.1016/j.atmosres.2021.105988
  18. Liu, Z., Yang, Q., Shao, J., Wang, G., & Liu, H. (2022). Improving daily precipitation estimation in the data scarce area by merging rain gauge and TRMM data with a transfer learning framework. Journal of Hydrology, 613(PB), 128455. https://doi.org/10.1016/j.jhydrol.2022.128455
  19. Lujano Laura, E., Felipe Obando, O. G., Lujano Laura, A., & Quispe Aragón, J. (2015). Validación de la precipitación estimada por satélite TRMM y su aplicación en la modelación hidrológica del rio Ramis Puno Perú. Revista Investigaciones Altoandinas - Journal of High Andean Investigation, 17(2), 221. https://doi.org/10.18271/ria.2015.116
  20. Meza, J. C. (2020). Análisis comparativo de los modelos digitales de elevaciones SRTM y MDE-Ar 2.0 para la identificación de áreas de peligrosidad por inundaciones y anegamientos en un área urbana de llanura. Geográfica Digital, 17(33), 44. https://doi.org/10.30972/geo.17334015
  21. Monteiro, E. S. V., Fonte, C. C., & de Lima, J. L. M. P. (2018). Analysing the potential of OpenStreetMap data to improve the accuracy of SRTM 30 DEM on derived basin delineation, slope, and drainage networks. Hydrology, 5(3), 1–27. https://doi.org/10.3390/hydrology5030034
  22. Moriasi, D. N., Gitau, M. W., Pai, N., & Daggupati, P. (2015). Hydrologic and water quality models: Performance measures and evaluation criteria. Transactions of the ASABE, 58(6), 1763–1785. https://doi.org/10.13031/trans.58.10715
  23. Mtibaa, S., & Asano, S. (2022). Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan. Journal of Hydrology: Regional Studies, 42(December 2021), 101134. https://doi.org/10.1016/j.ejrh.2022.101134
  24. Neitsch, S. L., Arnold, J. G., & Srinivasan, R. (2002). Pesticides fate and transport predicted by the Soil and water Assessment Tool (SWAT) Atrazine, Metolachlor and Trifluralin in the Sugar Creek Watershed. BRC Report, 3.
  25. Paredes Trejo, F. J., Barbosa, H. A., Peñaloza-Murillo, M. A., Alejandra Moreno, M., & Farías, A. (2016). Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmosfera, 29(4), 323–342. https://doi.org/10.20937/ATM.2016.29.04.04
  26. Paulhus, J. L. H., & Kohler, M. A. (1952). Monthly weather review. Journal of the Franklin Institute, 80(8), 129–133. https://doi.org/https://doi.org/10.1175/1520-0493(1952)080%3C0129:IOMPR%3E2.0.CO;2
  27. Perez Valdivia, C., Cade Menun, B., & Mc Martin, D. W. (2017). Hydrological modeling of the pipestone creek watershed using the Soil Water Assessment Tool (SWAT): Assessing impacts of wetland drainage on hydrology. Journal of Hydrology: Regional Studies, 14(October), 109–129. https://doi.org/10.1016/j.ejrh.2017.10.004
  28. Pino-Vargas, E., Chávarri-Velarde, E., Ingol-Blanco, E., Mejía, F., Cruz, A., & Vera, A. (2022). Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil’s Creek, Tacna, Peru. Hydrology, 9(1), 1–36. https://doi.org/10.3390/hydrology9010010
  29. Roa Lobo, J., & Kamp, U. (2008). Modelos de Elevación Digital (MED) a partir de sistemas satelitales. Una introducción y análisis comparativo en la cordillera de Mérida-Venezuela. Revista Geografica Venezolana, 49(1), 11–42.
  30. Shen, Z., Yong, B., Yi, L., Wu, H., & Xu, H. (2022). From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest? Atmospheric Research, 268(December 2021). https://doi.org/10.1016/j.atmosres.2022.106029
  31. Siev, S., Paringit, E. C., Yoshimura, C., & Hul, S. (2016). Seasonal changes in the inundation area and water volume of the Tonle Sap River and its floodplain. Hydrology, 3(4), 1–12. https://doi.org/10.3390/hydrology3040033
  32. Singh, L., & Saravanan, S. (2020). Simulation of monthly streamflow using the SWAT model of the Ib River watershed, India. HydroResearch, 3, 95–105. https://doi.org/10.1016/j.hydres.2020.09.001
  33. Solimine, S. L., Zhou, L., Raghavendra, A., & Cai, Y. (2022). Relationships between intense convection, lightning, and rainfall over the interior Congo Basin using TRMM data. Atmospheric Research, 273(September 2021), 106164. https://doi.org/10.1016/j.atmosres.2022.106164
  34. Song, Y. H., Chung, E. S., & Shahid, S. (2022). Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios. Science of the Total Environment, 838(February), 156162. https://doi.org/10.1016/j.scitotenv.2022.156162
  35. Sproles, E. A., Mullen, A., Hendrikx, J., Gatebe, C., & Taylor, S. (2020). Autonomous aerial vehicles (AAVS) as a tool for improving the spatial resolution of snow albedo measurements in mountainous regions. Hydrology, 7(3), 1–16. https://doi.org/10.3390/hydrology7030041
  36. Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., & Hsu, K. L. (2018). A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. Reviews of Geophysics, 56(1), 79–107. https://doi.org/10.1002/2017RG000574
  37. Swain, S., Mishra, S. K., Pandey, A., Pandey, A. C., Jain, A., Chauhan, S. K., & Badoni, A. K. (2022). Hydrological modelling through SWAT over a Himalayan catchment using high-resolution geospatial inputs. Environmental Challenges, 8(July), 100579. https://doi.org/10.1016/j.envc.2022.100579
  38. Tam, T. H., Abd Rahman, M. Z., Harun, S., Hanapi, M. N., & Kaoje, I. U. (2019). Application of satellite rainfall products for flood inundation modelling in Kelantan River Basin, Malaysia. Hydrology, 6(4). https://doi.org/10.3390/HYDROLOGY6040095
  39. Valeh, S., Motamedvairi, B., Kiadaliri, H., & Ahmadi, H. (2021). Hydrological simulation of Ammameh basin by artificial neural network and SWAT models. Physics and Chemistry of the Earth, 123(March), 103014. https://doi.org/10.1016/j.pce.2021.103014
  40. van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., & Srinivasan, R. (2006). A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324(1–4), 10–23. https://doi.org/10.1016/j.jhydrol.2005.09.008
  41. van Tol, J., van Zijl, G., & Julich, S. (2020). Importance of detailed soil information for hydrological modelling in an urbanized environment. Hydrology, 7(2), 1–15. https://doi.org/10.3390/HYDROLOGY7020034
  42. Vu, T. T., Li, L., & Jun, K. S. (2018). Evaluation of multi-satellite precipitation products for streamflow simulations: A case study for the Han River Basin in the Korean Peninsula, East Asia. Water (Switzerland), 10(5). https://doi.org/10.3390/w10050642
  43. Wörner, V., Kreye, P., & Meon, G. (2019). Effects of bias-correcting climate model data on the projection of future changes in high flows. Hydrology, 6(2). https://doi.org/10.3390/hydrology6020046
  44. Xiang, S., Li, Y., Zhai, S., & Peng, J. (2021). Comparative analysis of precipitation structures in two Southwest China Vortex events over eastern Sichuan Basin by TRMM. Journal of Atmospheric and Solar-Terrestrial Physics, 221(June), 105691. https://doi.org/10.1016/j.jastp.2021.105691
  45. Zhang, X., Srinivasan, R., Zhao, K., & Van Liew, M. (2008). Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. HYDROLOGICAL PROCESSES, 20 August 2008. https://doi.org/10.1002/hyp
  46. Zhang, Y., Wu, C., Yeh, P. J. F., Li, J., Hu, B. X., Feng, P., & Jun, C. (2022). Evaluation and comparison of precipitation estimates and hydrologic utility of CHIRPS, TRMM 3B42 V7 and PERSIANN-CDR products in various climate regimes. Atmospheric Research, 265(June 2021), 105881. https://doi.org/10.1016/j.atmosres.2021.105881