Rainfall modeling with CMIP6-DCPP outputs and local characteristic information using eigenvector spatial filtering varying coefficient (ESF-VC)
DOI:
https://doi.org/10.54386/jam.v26i3.2599Keywords:
Rainfall, CMIP6, DCPP, Eigenvector Spatial Filtering, Varying coefficientAbstract
Estimating rainfall at a point or region is difficult because complex factors affect rainfall. A helpful strategy is to utilize the GCM output information from CMIP6-DCPP by forming a functional relationship between GCM output data and rainfall data at a certain point or region, called statistical downscaling. However, because the resolution of the GCM output is relatively low, the model could not explain the local effects since the heterogeneity is enormous. Based on this fact, the current research proposes to add some local characteristics in the downscaling model to improve the performance to predict the rainfall levels. Further, the rainfall levels have spatial dependencies among points. Therefore, this research employed the Eigenvector Spatial Filtering-Varying Coefficient (ESF-VC) as the methodology of the modeling. The objective of this research is to perform rainfall predictive modeling with CMIP6-DCPP output and some local characteristic information as predictors using ESF-VC methodology. The approach was implemented to predict the rainfall level in the Province of Riau in Indonesia. Based on the results, the ESF-VC model provides good performance in estimating rainfall in Riau. The variables that provide local effects are altitude, equator (location), equator (distance), and wet month dummy. While the variables ENSO and vegetation (NDVI) have a significant global effect on the model.
References
BMKG. (2023). Daftar Istilah Klimatologi. Balai Besar MKG Wilayah III. https://balai3.denpasar.bmkg.go.id/daftar-istilah-musim#
BMKG. (2024). Sekilas Tentang ENSO. https://www.bmkg.go.id/iklim/elnino.bmkg
Boer, G. J., Smith, D. M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., Kushnir, Y., Kimoto, M., Meehl, G. A., Msadek, R., Mueller, W. A., Taylor, K. E., Zwiers, F., Rixen, M., Ruprich-Robert, Y. and Eade, R. (2016). The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geosci. Model Develop., 9(10): 3751-3777. https://doi.org/10.5194/gmd-9-3751-2016
Djuraidah, A., Suheni, C. and Nabila, B. (2019). Peramalan Curah Hujan Ekstrim di Provinsi Banten dengan Model Ekstrim Spasial. Media Statistika, 12(1): 50. https://doi.org/10.14710/medstat.12.1.50-62
Fotheringham, A. S., Brunsdon, C. and Charlton, M. (2002). Geographical Weighted Regression. John Wiley & Sons Ltd.
Goswami, P., Saha, S., Lalu Das, and Saon Banerjee (2024). Evaluation of CMIP6 GCMs performance and future projection for the Boro and Kharif seasons over the new alluvial zones of West Bengal. J. Agrometeorol., 26(2):168-173. https://doi.org/10.54386/jam.v26i2.2485
Griffith, D. A. (2000). A linear regression solution to the spatial autocorrelation problem. J. Geograph. Syst., 2(2): 141–156. https://doi.org/10.1007/pl00011451
IPCC. (2023). What is a GCM? IPCC Data Distribution Centre. https://www.ipcc-data.org/guidelines/pages/gcm_guide.html
Kardiana, A., Wigena, A. H., Djuraidah, A. and Soleh, A. M. (2022). Statistical Downscaling Modeling for Monthly Rainfall Estimation using Geographical And Temporal Weighted Gamma Regression. Asian J. Math. Computer Res., 29(2):43-55. https://doi.org/10.56557/ajomcor/2022/v29i27923
Kurnia, W. G. and Agdialta, R. (2020). Analisis Perubahan Vegetasi dan Variabilitas Curah Hujan di Kawasan Taman Nasional Lore Lindu, Sulawesi Tengah. Buletin GAW Bariri, 1(1), 47–57. https://doi.org/10.31172/bgb.v1i1.10
Lesik, E. M., Sianturi, H. L., Geru, A. S. and Bernandus, B. (2020). Analisis Pola Hujan Dan Distribusi Hujan Berdasarkan Ketinggian Tempat Di Pulau Flores. Jurnal Fisika : Fisika Sains Dan Aplikasinya, 5(2): 118-128. https://doi.org/10.35508/fisa.v5i2.2451
Mukherjee, A., Banerjee, S., Saha, S., Nath, R., Naskar, M. K. and Mukherjee, A (2024). Developing weather-based biomass prediction equation to assess the field pea yield under future climatic scenario. J. Agrometeorol., 26(1): 45-50. https://doi.org/10.54386/jam.v26i1.2461
Murakami, D. and Griffith, D. A. (2015). Random effects specifications in eigenvector spatial filtering: a simulation study. J. Geograph. Syst., 17(4): 311-331. https://doi.org/10.1007/s10109-015-0213-7
Murakami, D. and Griffith, D. A. (2019). Spatially varying coefficient modeling for large datasets : Eliminating N from spatial regressions. 30: 39-64. https://doi.org/10.1016/j.spasta.2019.02.003
Murakami, D., Lu, B., Harris, P., Brunsdon, C., Charlton, M., Nakaya, T. and Griffith, D. A. (2018). The Importance of Scale in Spatially Varying Coefficient Modeling. Annals of the American Assoc. Geograph., 109(1): 50-70. https://doi.org/10.1080/24694452.2018.1462691
Ratag, M. A. (2001). Reanalisis curah hujan di Indonesia hasil simulasi model area terbatas resolusi tinggi CSIRO DARLAM. Temu Ilmiah Prediksi Cuaca Dan Iklim Nasional 2000.
Sur, K., Dave, R. and Chauhan, P. (2018). Spatio - Temporal changes in NDVI and rainfall over Western Rajasthan and Gujarat region of India. J. Agrometeorol., 20(3): 183-189. https://doi.org/10.54386/jam.v20i3.541
Tukidin. (2010). Karakter Curah Hujan di Indonesia. Jurnal Geografi, 7(2: 136-145.
Wigena, A. H. and Djuraidah, A. (2022). Monograph Pengembangan Statistical Downscaling untuk Peningkatan Akurasi Prediksi Curah Hujan. IPB Press.
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