Using Smart Climate-Based Technology to Predict Water Availability in a Volcanic Landscape of Central Java, Indonesia – A Pathway to Conservation Strategy

• Autorzy: Pulungan Nur Ainun Harlin Jennie , Islami Jihan Dwi , Muttaqin Andi Syahid , Sartohadi Junun

Identyfikatory

DOI

10.12912/27197050/191536

• Języki publikacji: EN

Abstrakty

EN

High need for precision agriculture today has been associated with the completeness of the data available. The application of smart technology becomes the main alternative for fulfilling incomplete data and predicting future data. This paper presents the results of a research that aims to monitor and to predict water availability for determining conservation strategy on water availability using time series data from satellite rainfall, spanning from 2007 to 2022. We employed the innovative use of remote sensing technology, global rainfall measurement (GPM) and Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), to assess and correct satellite-derived rainfall estimates against ground-based observations. Conducted in the Serang watershed of Mount Merbabu’s volcanic landscape, the research employs a quantitative descriptive approach. We used coupled climate models MIROC6 and MRI-ESM2-0 in the CMIP-6 Framework for rainfall projections 2023–2030, then continued with the F.J. Mock model simulations for water availability projection. Our findings reveal a significant impact of climate change on water availability over the decade, with the most extreme conditions observed in 2029 and 2030, where the increasing of water availability reaching 10 m³ /s. The results showed that CHIRPS performed well in describing rainfall data. The novelty of this research highlighting the potential of: firstly, the use of satellite rainfall data for this specific region has not been extensively studied before; secondly, the discovered impacts of climate change to the water availability are particularly noteworthy, and thus contributing to the sustainability of agricultural practices in response to climate change. A limitation of this study is the short investigation period of just one decade, which does not fully capture the long-term impacts of climate change. Future research is recommended to utilize satellite data over extended periods to better represent extreme climate events and derive drought and wetness patterns over durations exceeding one decade.

Słowa kluczowe

EN

climate smart agriculture; rainfall projection; remote sensing technology; satellite rainfall data; Serang watershed; water availability

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 10
• Strony: 216--229
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Pulungan Nur Ainun Harlin Jennie

• Department of Soil, Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia

• https://orcid.org/0000-0002-9889-1600

autor

Islami Jihan Dwi

• Department of Soil, Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia

• https://orcid.org/0009-0002-9339-9147

autor

Muttaqin Andi Syahid

• Department of Soil, Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia

• https://orcid.org/0000-0001-6373-1255

autor

Sartohadi Junun

• Department of Soil, Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia

• https://orcid.org/0000-0002-0059-8335

Bibliografia

• 1. Collischonn, B., Collischonn, W., Tucci, C.E.M. 2008. Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates. Journal of Hydrology, 360(1–4), 207–216. https://doi.org/10.1016/j.jhydrol.2008.07.032
• 2. Chellamuthu, S.N., Ganapathy, G.P., 2024. Quantifying the impact of changing rainfall patterns on landslide frequency and intensity in the Nilgiris District of Western Ghats, India, Progress in Disaster Science, 3 https://doi.org/10.1016/j.pdisas.2024.100351
• 3. Damayanti, A.C., Limantara, L.M., Haribowo, R. 2022. Analisis debit banjir rancangan dengan metode HSS Nakayasu, HSS ITB-1, dan HSS Limantara pada DAS Manikin di Kabupaten Kupang. Jurnal Teknologi Dan Rekayasa Sumber Daya Air, 2(2), 313. https://doi.org/10.21776/ub.jtresda.2022.002.02.25
• 4. Dwiratna N.P.S., Nawawi, G. dan Asdak, C. 2014. Analisis curah hujan dan aplikasinya dalam penetapan jadwal dan pola tanam pertanian lahan kering di Kabupaten Bandung. Bionatura-Jurnal Ilmu-Ilmu Hayati Dan Fisik, 16(2), 95–102.
• 5. Fadholi, A., Sari, F.P., Aji, P., Dewi, R. 2014. Pemanfaatan model Weather Research and Forecasting (WRF) dalam Analisis Cuaca terkait Hujan Lebat Batam 30-31 Januari 2011. Jurnal Fisika Dan Aplikasinya, 10(1), 24. https://doi.org/10.12962/j24604682.v10i1.820
• 6. Fadli, D.M. 2022. Pemanfaatan data satelit terhadap prediksi curah hujan. Jurnal Konstruksi, 20(1), 194–201. https://doi.org/10.33364/konstruksi/v.20-1.1107
• 7. Fauzi, M., Darmayanti, L., Hilwan, F. 2013. Pengisian Kekosongan Data Hujan Dengan Metode Multiple Nonlinier Standardize Corelation Pada Stasiun Hujan Daerah Aliran Sungai Indragiri Dan Rokan. Semantic Scholar, 2, 1–7.
• 8. Haddeland, I., Heinke, J., Biemans, H., Eisner, S., Flörke, M., Hanasaki, N., Konzmann, M., Ludwig, F., Masaki, Y., Schewe, J., Stacke, T., Tessler, Z.D., Wada, Y., Wisser, D. 2014. Global water resources affected by human interventions and climate change. Proceedings of the National Academy of Sciences of the United States of America, 111(9), 3251–3256. https://doi.org/10.1073/pnas.1222475110
• 9. Haslinger, K., Koffler, D., Schoner, W., Laaha, G. 2014. Exploring the link between meteorological drought and streamflow: Effects of climate-catchment interaction. Water Resources Research, 5375–5377. https://doi.org/10.1002/2013WR014979 Reply
• 10. Hasyimzoem, E.F., Zakaria, A., Sumiharni. 2021. Perbandingan analisis data curah hujan yang hilang menggunakan metode normal ratio, inversed square distance, Rata-Rata Al-jabar, dan Regresi Berganda. Jurnal Rekayasa Sipil, 7(1), 155–162. http://journal.eng.unila.ac.id/index.php/jrsdd/article/view/1322
• 11. Indarto, I. 2012. Prosedur kalibrasi dan validasi model SMAR untuk mendeskripsikan proses hujan aliran di Sub-DAS Rawatamtu. Jurnal Teknik Sipil, 19(3), 265. https://doi.org/10.5614/jts.2012.19.3.7
• 12. Jarwanti, D.P., Suhartanto, E., Fidari, J.S. 2021. Validasi data curah hujan satelit TRMM (Tropical Rainfall Measuring Mission) dengan data pos penakar hujan di DAS Grindulu, Kabupaten Pacitan, Jawa Timur. Jurnal Teknologi Dan Rekayasa Sumber Daya Air, 1(2), 772–785.
• 13. Kataoka, T., Tatebe, H., Koyama, H., Mochizuki, T., Ogochi, K., Naoe, H., Imada, Y., Shiogama, H., Kimoto, M., Watanabe, M. 2020. Seasonal to decadal predictions with MIROC6: description and basic evaluation. Journal of Advances in Modeling Earth Systems, 12(12), 1–25. https://doi.org/10.1029/2019MS002035
• 14. Kawai, H., Yukimoto, S., Koshiro, T., Oshima, N., Tanaka, T., Yoshimura, H., Nagasawa, R. 2019. Significant improvement of cloud representation in the global climate model MRI-ESM2. Geoscientific Model Development, 12(7), 2875–2897. https://doi.org/10.5194/gmd-12-2875-2019
• 15. Laimeheriwa, S., Pangaribuan, M., Amba, M. 2019. analisis fenomena El Nino dan dampaknya terhadap neraca air lahan di pulau Ambon. Jurnal Budidaya Pertanian, 15(2), 111–118. https://doi.org/10.30598/jbdp.2019.15.2.111
• 16. López-Moreno, J.I., Zabalza, J., Vicente-Serrano, S.M., Revuelto, J., Gilaberte, M., Azorin-Molina, C., Morán-Tejeda, E., García-Ruiz, J.M., Tague, C. 2014. Impact of climate and land use change on water availability and reservoir management: Scenarios in the Upper Arago´n River, Spanish Pyrenees. Science of the Total Environment, 493, 1222–1231. https://doi.org/10.1016/j.scitotenv.2013.09.031
• 17. Lorenzo-Lacruz, J., Vicente-Serrano, S.M., González-Hidalgo, J.C., López-Moreno, J.I., Cortesi, N. 2013. Hydrological drought response to meteorological drought in the Iberian Peninsula. Climate Research, 58(2), 117–131. https://doi. org/10.3354/cr01177
• 18. Mamenun, Pawitan, H., Sophaheluwakan, A. 2014. Validasi dan koreksi data satelit TRMM pada tiga pola hujan di Indonesia (Validation and correction of TRMM satellite data on three rainfall patterns in Indonesia). Jurnal Meteorologi Dan Geofisika, 15(1), 13–23. http://202.90.199.54/jmg/index.php/jmg/article/view/169/155
• 19. Marzuki, Randeu, W.L., Kozu, T., Shimomai, T., Hashiguchi, H., Schönhuber, M. 2013. Raindrop axis ratios, fall velocities and size distribution over Sumatra from 2D-Video Disdrometer measurement. Atmospheric Research, 119, 23–37. https:// doi.org/10.1016/j.atmosres.2011.08.006
• 20. Misnawati, M., Boer, R., June, T., Faqih, A. 2018. perbandingan metodologi koreksi bias data curah hujan CHIRPS. Limnotek, 25(1), 18–29.
• 21. Ouharba, E.H., Mabrouki, J., Triqui, Z.E.A., 2024. Assessment and future climate dynamics in the Bouregreg Basin, Morocco - Impacts and adaptation alternatives. Ecol. Eng. Environ. Technol. 25(3), 51–63 https://doi.org/10.12912/27197050/177823
• 22. Partarini, N.M.C., Sujono, J., Pratiwi, E.P.A. 2021. Koreksi dan Validasi data curah hujan satelit GPMIMERG dan CHIRPS di Das Selorejo, Kabupaten Malang. Prosiding CEEDRiMS, 1(1), 149–156. https://giovanni.gsfc.nasa.gov/
• 23. Putri, D., Perdinan. 2018. Analysis of regional water availability for domestic water demand (Case Study: Malang Regency). Agromet, 32(2), 93. https://doi.org/10.29244/j.agromet.32.2.93-102
• 24. Radhika, Fauzi, M., Rahmawati, S., Rendy, F., Anthon, F., Hatmoko, W. 2012. Neraca ketersediaan air permukaan dan kebutuhan air pada wilayah sungai di Indonesia. Kolokium Hasil Penelitian Dan Pengembangan Sumber Daya Air2, 193, 1–14.
• 25. Radhika, R., Firmansyah, R., Hatmoko, W. 2017. Perhitungan ketersediaan air permukaan di Indonesia berdasarkan data satelit. Jurnal Sumber Daya Air, 13(2), 115–130. https://doi.org/10.32679/jsda.v13i2.206
• 26. Ramzi, K., Nadir, M., Tewfik, B.M., Hakim, D.K. 2024. Hydrological forecasts modeling using artificial intelligence and conceptual models of KébirRhumel Watershed, Algeria, Ecol. Eng. Environ. Technol. 25(9), 68–80.
• 27. Rahma, N.F., Suhartanto, E., Harisuseno, D. 2019. Validasi data curah hujan TRMM (Tropical Rainfall Measuring Mission) dengan Pos Stasiun Hujan di Sub DAS Sumber Brantas. Jurnal Mahasiswa Teknik Pengairan Universitas Brawijaya, 2(2), 1–13.
• 28. Romadhoniastri, S., Ayumi, N., Ulumia, F., Zahro, N.A., Rahayuli, R., Wahyudi, R., Hayat, D.M., Hadi, M.P. 2022. Kajian karakteristik aliran sungai serang di AWLR bendungan kulonprogo berdasarkan pemodelan hidrologi HEC-HMS. Jurnal Geografi : Media Informasi Pengembangan Dan Profesi Kegeografian, 19(2), 54–61. https://doi.org/10.15294/jg.v19i2.33671
• 29. Stodolak, R., Baran, J., Knap, E., 2018. The influence of rain temopration on the results of rainfallrunoff model. Inż. Ekolog. 19(6), 87–93 https://doi.org/10.12912/23920629/95271
• 30. Sadeghi, M., Asanjan, A.A., Faridzad, M., Nguyen, P.H.U., Hsu, K., Sorooshian, S., Braithwaite, D.A.N. 2019. PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks–convolutional neural networks. Journal of Hydrometeorology, 20(12), 2273–2289. https://doi.org/10.1175/JHM-D-19-0110.1
• 31. Setiyawan, Andiese, V.W., Anzar, L.A. 2017. Analisis ketersediaan air dengan metode F.J mock pada daerah persawahan desa Poboya Palu Sulawesi Tengah. Infrastruktur, 7(1), 18–26.
• 32. Simanjuntak, J.T., Azka, M.A., Dzikiro, T.K., Hariadi. 2018. Analisis dampak fenomena El Nino terhadap ketersediaan air tanah di Bima, NTB Pada Tahun 2015. Seminar Nasional Pelestarian Lingkungan, May, 187–193.
• 33. Susanti, E., Surmaini, E., Pramudia, A., Heryani, N., Estiningtyas, W., Suciantini, S., Apriyana, Y. 2021. Pemutakhiran peta sumberdaya agroklimat indonesia untuk mendukung perencanaan pertanian. Jurnal Tanah Dan Iklim, 45(1), 47. https://doi.org/10.21082/jti.v45n1.2021.47-58
• 34. Tabari, H. 2020. Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10, 13768. https://doi.org/10.1038/s41598-020-70816-2
• 35. Tavosi, M., Vafakhah, M., Shekohideh, M., Sadeghi, S., Moosavi, V., Zheng. Z., Yang. Q., 2024. Rainfall extreme indicators trend and meteorological drought changes under climate change scenarios, Water Resources Management https://doi.org/10.1007/s11269-024-03871-3
• 36. Tang, L., Tian, Y., Yan, F., Habib, E. 2015. An improved procedure for the validation of satellite-based precipitation estimates. Atmospheric Research, 163, 61–73. https://doi.org/10.1016/j.atmosres.2014.12.016
• 37. Tanzeeba, S., Gan, T.Y. 2012. Potential impact of climate change on the water availability of South Saskatchewan River Basin. Climatic Change, 112(2), 355–386. https://doi.org/10.1007/s10584-011-0221-7
• 38. Triyanto, S., Sunyoto, A., Arief, M.R. 2021. Analisis klasifikasi bencana banjir berdasarkan curah hujan menggunakan algoritma naïve Bayes. JOISIE (Journal Of Information Systems And Informatics Engineering), 5(2), 109–117. https://doi.org/10.35145/joisie.v5i2.1785
• 39. Wu, D., Chen, J., Xiong, L., Lee, J., Kim, J., Moon, H., 2024. Assessing global drought conditions under climate change: A comparison of stationary and non-stationary approaches and identification of hotspot regions, Journal of Hydrology, 640, https://doi.org/10.1016/j.jhydrol.2024.131663
• 40. Yang, N., Zhang, K., Hong, Y., Zhao, Q., Huang, Q., Xu, Y., Xue, X., Chen, S. 2017. Evaluation of the TRMM multisatellite precipitation analysis and its applicability in supporting reservoir operation and water resources management in Hanjiang basin, China. Journal of Hydrology, 549, 313–325. https://doi.org/10.1016/j.jhydrol.2017.04.006
• 41. Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., Ishii, M. 2019. The meteorological research institute Earth system model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan, 97(5), 931–965. https://doi.org/10.2151/jmsj.2019-051