A Spatiotemporal Analysis of Humidity Pattern in Bali using Space-Time Kriging with Seasonal Drift

Salma Fitri Nugroho; Rahma Fitriani; Atiek Iriany

doi:10.31764/jtam.v9i3.30874

A Spatiotemporal Analysis of Humidity Pattern in Bali using Space-Time Kriging with Seasonal Drift

Salma Fitri Nugroho, Rahma Fitriani, Atiek Iriany

Abstract

Climate plays a vital role in framing the characteristics of tourist activity. Humidity reflects the amount of moisture in the air relative to the maximum it can hold at a specific temperature, it has a direct influences on perceived comfort levels. Bali, one of the most popular destinations renowned for its breathtaking natural beauty and varied landscapes. However, this island is currently served by only four climate observation stations which are insufficient to capture the humidity across the island. Therefore, this research aims to model humidity levels in Bali based on four observed locations at 2019-2023 using the space-time kriging with seasonal drift and predict humidity at unobserved locations. This approach was choosen due to the strong seasonal pattern exhibited in climate data, which leading to non-stationary. The space-time kriging method is applied to the residuals. The most effective model identified was the exponential-exponential-Gaussian (Exp-Exp-Gau) model using a sum-metric structure. This model provided the lowest RMSE of 2.1442. Humidity contour maps suggest a gradual decline in humidity levels over time across Bali. This trend may have significant impacts for both environmental quality and the tourism sector. Lower humidity levels could lead to increased discomfort for tourists and potentially reduce the attractiveness of the destination. Theoretically, the development of the kriging model enhances the accuracy of predictions, as shows by the low RMSE. Practically, these findings emphasize the importance of integrating climate considerations into sustainable tourism planning and management strategies based on the humidity information.

Keywords

Humidity; Interpolation; Kriging; Space-time.

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References

Adhikary, S. K., Muttil, N., & Yilmaz, A. G. (2017). Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments. Hydrological Processes, 31(12), 2143–2161. https://doi.org/10.1002/hyp.11163

Amelia, R., Julianti, E., & Guskarnali. (2023). Implementation of Inverse Distance Weighting (IDW) and Kriging method for distribution pattern humidity and temperature data on weather changes in the Bangka Islands. IOP Conference Series: Earth and Environmental Science, 1267(1), 012091. https://doi.org/10.1088/1755-1315/1267/1/012091

Ananta, M. I., Limantara, L. M., & Soetopo, W. (2024). The Impact of Climate Change on Air Temperature in the Rainy and Dry Seasons in East Java, Indonesia: A Case Study of Climate Change in the Wlingi Dam Area. International Journal of Environmental Impacts, 7(2), 169–180. https://doi.org/10.18280/ijei.070202

Atasoy, M., & Guneysu Atasoy, F. (2020). The Impact of Climate Change on Tourism: A Causality Analysis. Turkish Journal of Agriculture - Food Science and Technology, 8(2), 515–519. https://doi.org/10.24925/turjaf.v8i2.515-519.3250

Bachrudin, A., Ruchjana, B. N., Abdullah, A. S., & Budiarto, R. (2023). Spatio-Temporal Model of a Product–Sum Simulation on Stream Network Based on Hydrologic Distance. Water, 15(11), 2039. https://doi.org/10.3390/w15112039

Baldwin, J. W., Benmarhnia, T., Ebi, K. L., Jay, O., Lutsko, N. J., & Vanos, J. K. (2023). Humidity’s Role in Heat-Related Health Outcomes: A Heated Debate. Environmental Health Perspectives, 131(5). https://doi.org/10.1289/ehp11807

Das, P., & Barman, S. (2025). Perspective Chapter: An Overview of Time Series Decomposition and Its Applications. In Applied and Theoretical Econometrics and Financial Crises [Working Title]. IntechOpen. https://doi.org/10.5772/intechopen.1009268

Dhaher, G., & Shexo, A. (2023). Using Kriging Technique to Interpolate and Forecasting Temperatures Spatio-Temporal Data. European Journal of Pure and Applied Mathematics, 16(1), 373–385. https://doi.org/10.29020/nybg.ejpam.v16i1.4613

Ding, Q., Wang, Y., Zheng, Y., Wang, F., Zhou, S., Pan, D., Xiong, Y., & Zhang, Y. (2024). Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression. Fractal and Fractional, 8(12), 717. https://doi.org/10.3390/fractalfract8120717

Du, Z., Wu, S., Kwan, M.-P., Zhang, C., Zhang, F., & Liu, R. (2018). A spatiotemporal regression-kriging model for space-time interpolation: A case study of chlorophyll-a prediction in the coastal areas of Zhejiang, China. International Journal of Geographical Information Science, 32(10), 1927–1947. https://doi.org/10.1080/13658816.2018.1471607

Jong, M.-C., Puah, C.-H., & Arip, M. A. (2024). The Impact of Climate Change on Tourism Demand in China. International Journal of Energy Economics and Policy, 14(3), 482–488. https://doi.org/10.32479/ijeep.14149

Khan, M., Almazah, M. M. A., EIlahi, A., Niaz, R., Al-Rezami, A. Y., & Zaman, B. (2023). Spatial interpolation of water quality index based on Ordinary kriging and Universal kriging. Geomatics, Natural Hazards and Risk, 14(1), 2190853. https://doi.org/10.1080/19475705.2023.2190853

Krock, M., Bessac, J., Stein, M. L., & Monahan, A. H. (2022). Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails. Weather and Climate Extremes, 36, 100438. https://doi.org/10.1016/j.wace.2022.100438

Lambardi Di San Miniato, M., Bellio, R., Grassetti, L., & Vidoni, P. (2022). Separable spatio‐temporal kriging for fast virtual sensing. Applied Stochastic Models in Business and Industry, 38(5), 806–829. https://doi.org/10.1002/asmb.2697

Li, S., Griffith, D. A., & Shu, H. (2020). Temperature prediction based on a space–time regression-kriging model. Journal of Applied Statistics, 47(7), 1168–1190. https://doi.org/10.1080/02664763.2019.1671962

Liu, D. (2024). The prediction and analysis of global climate change based on SARIMA. Applied and Computational Engineering, 40(1), 268–273. https://doi.org/10.54254/2755-2721/40/20230665

Lukić, D., Petrović, M. D., Radovanović, M. M., & Tretiakova, T. N. (2021). The role of TCI and TCCI indexes in regional tourism planning. European Journal of Geography, 12(4), 6–15. https://doi.org/10.48088/ejg.d.luk.12.4.006.015

Lv, J., & Du, S. (2021). Kriging Method-Based Return Prediction of Waste Electrical and Electronic Equipment in Reverse Logistics. Applied Sciences, 11(8), 3536. https://doi.org/10.3390/app11083536

Mondal, S. K., Ahmmad, M. S., Khan, S., Chowdhury, M. H., Paul, G. K., Binyamin, Md., Gupta, P. S., Purohit, S., & Chakrabortty, R. (2025). Modeling and Forecasting Monthly Humidity in South Asia: A SARIMA Approach. Earth Systems and Environment. https://doi.org/10.1007/s41748-025-00607-0

O’Rourke, S., & Kelly, G. E. (2015). Spatio-temporal Modelling of Forest Growth Spanning 50 Years – The Effects of Different Thinning Strategies. Procedia Environmental Sciences, 26, 101–104. https://doi.org/10.1016/j.proenv.2015.05.008

Rahmawati, N. (2020). Space-time variogram for daily rainfall estimates using rain gauges and satellite data in mountainous tropical Island of Bali, Indonesia (Preliminary Study). Journal of Hydrology, 590, 125177. https://doi.org/10.1016/j.jhydrol.2020.125177

Ramadhani, D. P., Alamsyah, A., Febrianta, M. Y., & Damayanti, L. Z. A. (2024). Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 743–773. https://doi.org/10.3390/jtaer19020040

Ro, Y., & Yoo, C. (2022). Numerical Experiments Applying Simple Kriging to Intermittent and Log-Normal Data. Water, 14(9), 1364. https://doi.org/10.3390/w14091364

Simorangkir, C. O., Ramadhan, G., Sukran, M. A., & Manalu, T. (2024). Tourism Development Impact on Economic Growth and Poverty Alleviation in West Java. Jurnal Kepariwisataan Indonesia: Jurnal Penelitian Dan Pengembangan Kepariwisataan Indonesia, 18(2), 175–196. https://doi.org/10.47608/jki.v18i22024.175-196

Susanto, J., Zheng, X., Liu, Y., & Wang, C. (2020). The Impacts of Climate Variables and Climate-Related Extreme Events on Island Country’s Tourism: Evidence from Indonesia. Journal of Cleaner Production, 276, 124204. https://doi.org/10.1016/j.jclepro.2020.124204

Taharin, M. R., & Roslee, R. (2021). The Application of Semi Variogram and Ordinary Kriging in Determining the Cohesion and Clay Percentage Distribution in Hilly Area of Sabah, Malaysia. International Journal of Design & Nature and Ecodynamics, 16(5), 525–530. https://doi.org/10.18280/ijdne.160506

Toersilowati, L., Siswanto, B., Maryadi, E., Susanti, I., Suhermat, M., Witono, A., Sipayung, S. B., Rahayu, S. A., ‘Adany, F., Aminuddin, J., & Lubis, R. F. (2022). The Projections of Climate Change using Conformal Cubic Atmospheric Model (CCAM) in Bali—Indonesia. IOP Conference Series: Earth and Environmental Science, 1047(1), 012033. https://doi.org/10.1088/1755-1315/1047/1/012033

Venkatachalam, P., & Kumar, M. (2017). Space-Time Variogram Modeling. In S. Shekhar, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 1932–1938). Springer International Publishing. https://doi.org/10.1007/978-3-319-17885-1_1643

Zaharieva, S., Georgiev, I., Georgiev, S., Borodzhieva, A., & Todorov, V. (2025). A Method for Forecasting Indoor Relative Humidity for Improving Comfort Conditions and Quality of Life. Atmosphere, 16(3), 315. https://doi.org/10.3390/atmos16030315

Zeiss, H., Graw, K., & Matzarakis, A. (2022). Impact of the Destination Weather Conditions on Decision and Complaint Behavior of Guests Who Booked Vacation Rentals. Atmosphere, 13(12), 1998. https://doi.org/10.3390/atmos13121998

Zeng, Y., Filimonau, V., Wang, L., & Zhong, L. (2023). The impact of perceived unfavorable weather on tourist loyalty in high-altitude destinations: The case of the Qinghai-Tibet plateau, China. Journal of Outdoor Recreation and Tourism, 43, 100658. https://doi.org/10.1016/j.jort.2023.100658

Zhao, J., Li, C., Yang, T., Tang, Y., Yin, Y., Luan, X., & Sun, S. (2020). Estimation of high spatiotemporal resolution actual evapotranspiration by combining the SWH model with the METRIC model. Journal of Hydrology, 586, 124883. https://doi.org/10.1016/j.jhydrol.2020.124883

DOI: https://doi.org/10.31764/jtam.v9i3.30874