KARAKTER SPASIAL DAN TEMPORAL CURAH HUJAN BULANAN KABUPATEN JEMBER BERDASARKAN DATA CHIRPS

Authors

DOI:

https://doi.org/10.21776/ub.jtsl.2024.011.2.13

Keywords:

CHIRPS, Precipitation, Jember, Geographic Information System, Spatial and Temporal

Abstract

Jember Regency is one of the national food storage areas. One of the most important resources in cultivating food crops is water which comes from rainfall. The existence of rainfall data is very important in planning and implementing agricultural activities. Limited rainfall data is the main problem for agricultural planning. One solution to this problem is to utilize rainfall data from CHIRPS (Climate Hazards Group Infrared Precipitation Station). The use of CHIRPS data is still biased data, so bias correction is needed to measure the level of accuracy of CHIRPS data for estimating rainfall in an area. Bias correction is carried out using two methods, namely multiple linear regression and power regression. The correction results show that the multiple linear regression method is able to produce monthly rainfall that is close to observations with an RMSE value of 3.9 and a coefficient of determination of 0.99. Temporally, the peak of the rainy season in Jember Regency occurs in December while the dry season is in August. In the rainy season (December-January and February) Jember Regency's rainfall is around 300-500 mm/month and in the dry season (June-July-August) it is in the range of 50-200 mm/month. Spatially, the northern part of Jember, especially Sumberbaru, Bangsalsari and Tanggul subdistricts, is wetter than the Jember Regency area as a whole. The southern part of Jember, especially those close to the coast, has lower rainfall than other regions. In the dry season, rainfall in this region is less than 20 mm/month.

Downloads

Download data is not yet available.

References

Aksu, H., & Akgül, M. A. (2020). Performance evaluation of CHIRPS satellite precipitation estimates over Turkey. Theoretical and Applied Climatology, 142(1), 71-84. https://doi.org/10.1007/s00704-020-03301-5

Cavalcante, R. B. L., da Silva Ferreira, D. B., Pontes, P. R. M., Tedeschi, R. G., da Costa, C. P. W., & de Souza, E. B. (2020). Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia. Atmospheric Research, 238, 104879. https://doi.org/10.1016/j.atmosres.2020.104879

Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., & Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Quarterly Journal of the Royal Meteorological Society, 144, 292-312. https://doi.org/10.1002/qj.3244

Enyew B.D., Steeneveld, G.J. (2014). Analysing the Impact of Topography on Precipitation and Flooding on the Ethiopian Highlands. Journal of Geology & Geophysics, 3(6). doi: 10.4172/2329-6755.1000173

Geng, H., Pan, B., Huang, B., Cao, B., & Gao, H. (2017). The spatial distribution of precipitation and topography in the Qilian Shan Mountains, northeastern Tibetan Plateau. Geomorphology, 297, 43-54. https://doi.org/10.1016/j.atmosres.2020.105259

Goshime, D. W., Absi, R., & Ledésert, B. (2019). Evaluation and bias correction of CHIRP rainfall estimate for rainfall-runoff simulation over Lake Ziway watershed, Ethiopia. Hydrology, 6(3), 68. https://doi.org/10.3390/hydrology6030068

Hadi, S. J., & Tombul, M. (2018). Comparison of spatial interpolation methods of precipitation and temperature using multiple integration periods. Journal of the Indian Society of Remote Sensing, 46(7), 1187-1199. https://doi.org/10.1007/s12524-018-0783-1

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

Kumar K. P. and Barik D. K. (2015). Comparison of Agricultural Yield with and Without A Canal Head Regulator. International Journal of Advanced Technology in Engineering and Science, 3(9), 19-30.

Li, W., Sun, W., He, X., Scaioni, M., Yao, D., Chen, Y., ... & Cheng, G. (2019). Improving CHIRPS daily satellite-precipitation products using coarser ground observations. IEEE Geoscience and Remote Sensing Letters, 16(11), 1678-1682. https://doi.org/10.1109/LGRS.2019.2907532

McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179-183). https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf

Medina, F. D., Zossi, B. S., Bossolasco, A., & Elias, A. G. (2023). Performance of CHIRPS dataset for monthly and annual rainfall-indices in Northern Argentina. Atmospheric Research, 283, 106545.

Mishra, A. K., & Singh, V. P. (2011). Drought modeling–A review. Journal of Hydrology, 403(1-2), 157-175. https://doi.org/10.1016/j.jhydrol.2011.03.049

Misnawati, M., Boer, R., June, T., & Faqih, A. (2018). Perbandingan metodologi koreksi bias data curah hujan chirps. Limnotek: perairan darat tropis di Indonesia, 25(1). https://dx.doi.org/10.14203/limnotek.v25i1.224

Prakash, S. (2019). Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India. Journal of hydrology, 571, 50-59. https://doi.org/10.1016/j.jhydrol.2019.01.036

Sandeep, P., Reddy, G. O., Jegankumar, R., & Kumar, K. A. (2021). Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets. Ecological Indicators, 121, 107033. https://doi.org/10.1016/j.ecolind.2020.107033

Shen, Z., Yong, B., Gourley, J. J., Qi, W., Lu, D., Liu, J., ... & 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

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

Sirisena, T. A. J. G., Maskey, S., Ranasinghe, R., & Babel, M. S. (2018). Effects of different precipitation inputs on streamflow simulation in the Irrawaddy River Basin, Myanmar. Journal of Hydrology: Regional Studies, 19, 265-278. https://doi.org/10.1016/j.ejrh.2018.10.005

Wahyuni, S., Sisinggih, D., & Dewi, I. A. G. (2021, December). Validation of Climate Hazard Group InfraRed Precipitation with Station (CHIRPS) Data in Wonorejo Reservoir, Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 930, No. 1, p. 012042). IOP Publishing. https://doi.org/10.1088/1755-1315/930/1/012042

Wu, W., Li, Y., Luo, X., Zhang, Y., Ji, X., & Li, X. (2019). Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China. Geomatics, Natural Hazards and Risk, 10(1), 2145-2162. https://doi.org/10.1080/19475705.2019.1683082

Zargar, A., Sadiq, R., Naser, B., & Khan, F. I. (2011). A review of drought indices. Environmental Reviews, 19(NA), 333-349. https://doi.org/10.1139/a11-013

Downloads

Published

01-07-2024

Issue

Section

Articles

How to Cite

Purnamasari, I., Abdillah, M. R. W., Wijayanto, Y., Saputra, T. W., Ristiyana, S., & Budiman, S. A. (2024). KARAKTER SPASIAL DAN TEMPORAL CURAH HUJAN BULANAN KABUPATEN JEMBER BERDASARKAN DATA CHIRPS. Jurnal Tanah Dan Sumberdaya Lahan, 11(2), 423-432. https://doi.org/10.21776/ub.jtsl.2024.011.2.13

Similar Articles

1-10 of 98

You may also start an advanced similarity search for this article.