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All Journal Agromet Indonesian Journal of Geography Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sumberdaya Lahan Journal of Mathematical and Fundamental Sciences Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) LIMNOTEK - Perairan Darat Tropis di Indonesia International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL METEOROLOGI DAN GEOFISIKA Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sumberdaya Lahan Jurnal Tanah dan Iklim Jurnal Meteorologi dan Geofisika The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Akhmad Faqih
Agrometeorology Division, Department Of Geophysics And Meteorology, Faculty Of Mathematics And Natural Sciences, IPB University, Campus IPB Dramaga, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Energy Environmental Science Mathematics Physics

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IMPACT OF CHANGES IN CLIMATE AND LAND USE ON THE FUTURE STREAMFLOW FLUCTUATION Suria Tarigan; Akhmad Faqih
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 9 No. 1 (2019): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.9.1.181-189

Abstract

Beside land use change, future climate change potentially alters streamflow fluctuation of a river basin in Indonesia. We investigated relative impact of changes in climate and land use on the streamflow fluctuation of a watershed for future condition (2025). To account for the climate change, we simulated future rainfall and temperature scenarios using the downscaled rainfall and mean surface temperature of 24 CMIP5 GCM outputs with moderate scenario of RCP4.5. We used distributed hydrologic model (SWAT) to simulate relative impact of changes in climate and plantation expansion on the future streamflow fluctuation.  The SWAT model performed well with the Nash-Sutcliff efficiency values of 0.80-0.85 (calibration) and 0.84-0.86 (validation). The results indicated that the climate change caused 32% decrease of the low flows during dry season and 96% increase of the flooding peak discharge during rainy season. Meanwhile, the plantation expansion led to 40% decrease of the low flow in the dry season and 65% increase of the flooding peak discharge in wet season. Both changes indicated strong impact on the extreme events such as flooding peak discharge and low flows. The impact of the climate change on the increased peak discharge was stronger compared to that of land use change.  Meanwhile, the impact of the land use change on the low flow was stronger compared to that of the climate change. The results of this study pointed out that both climate change and the plantation expansion potentially become crucial factors for the future water security in Indonesia.
An Application of Deep Learning Technique to Improve Subseasonal to Seasonal Rainfall Forecast over Java Island, Indonesia Adyaksa Budi Raharja; Akhmad Faqih; Amsari Mudzakir Setiawan
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 12 No 4 (2022): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.12.4.587-598

Abstract

Subseasonal to seasonal (S2S) rainfall forecast can benefit several sectors, such as water resources, hazard management, and agriculture. However, the forecast remains challenging due to its lack of skill. This study applies Convolutional AutoEncoders (ConvAE), a deep learning technique, to improve the quality of the S2S rainfall forecast. Seven S2S model output incorporated with Subseasonal Experiments Projects (SubX), including CCSM4, CFSv2, FIMr1p1, GEFS, GEOS_v2p1, GEPS6, and NESM, are corrected using the ConvAE approach. We combine 407 ground observations and the CHIRPS dataset using regression kriging methods producing gridded daily precipitation data with 0.05° spatial resolution. We utilize this dataset as a label to train ConvAE models and to perform bias corrections to all members of the SubX forecasts data. The results show that ConvAE is able to increase the quality of weekly S2S rainfall forecasts over Java, Indonesia. The Correlation Coefficient for 1 – 4 weeks lead time are improved from: 0.76, 0.715, 0.692 and 0.722 towards 0.809, 0.751, 0.719 and 0.74, respectively. Furthermore, the average CRPSS improves between 20 – 30% for all lead times.
Co-Authors Adyaksa Budi Raharja Agita Vivi Wijayanti Agus Buono Alexander Knohl Alif Akbar Syafrianno Alif Kurniawan Alimatul Rahim Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Ardhasena Sopaheluwakan Ashehad Ashween Ali Bony Septian Pandjaitan Christian Stiegler Daniel Murdiyarso Dodo Gunawan Dodo Gunawan Eddy Hermawan Ela Hasri Windari Elza Surmaini Erna Nur Aini Fildza Novadiwanti Fithriya Yulisiasih Rohmawati Furqon Alfahmi Furqon Alfahmi Gita Adhani Jadmiko, Syamsu Dwi Kartika, Qurrata A'yun Laode Nurdiansyah M. Mukhlis Mamenun Mamenun Misnawati, Misnawati Mulyono R. Prabowo Mulyono R. Prabowo Naziah Madani P. Perdinan Rahmat Hidayat Rini Hidayati Rizaldi Boer Santikayasa, I Putu Sanusi Sanusi Sitanggang, Imas S. Supari Supari Suria Tarigan Syamsu Dwi Jadmiko Tania June Ummu Ma'rufah Yonny Koesmaryono