Coupling of Markov chains and cellular automata spatial models to predict land cover changes (case study: upper Ci Leungsi catchment area)

Marko, Kuswantoro; Zulkarnain, Faris; Kusratmoko, Eko

doi:10.1088/1755-1315/47/1/012032

Cited by 18 publications

(22 citation statements)

References 14 publications

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“…Once the transition probability matrix is known, then projection for land use in the next state is estimated using Eq. 2b (Hamad et al 2018;Liping et al 2018;Marko et al 2016):…”

Section: Projected Land Use Change With Markov Chain Analysismentioning

confidence: 99%

Adaptation by Himalayan Water Resource System under a Sustainable Socioeconomic Pathway in a High-Emission Context

2021

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Climate change in the Indian Himalayan region is being manifested in loss of glaciers and altered patterns of monsoon rainfall. Simultaneously, rapid population growth together with economic development are increasing sectoral water demands and changing land use patterns.This study investigated the impact of this complex interplay on water resources in the Beas-Sutlej water resources system. The GFDL-CM3 model was used to describe RCP8.5 future meteorological conditions throughout the 21 st century. Population and land use changes were projected under the Shared Socioeconomic Pathway-1 (SSP1). The Water Evaluation And Planning (WEAP) system was applied for assessing sectoral water demands. The results showed increasing runoff during the pre-monsoon and monsoon seasons, due respectively to increased glaciers melting and more rainfall. It also emerged that irrigation water demand will decrease moderately in Punjab (8 -13%) and Haryana (1 -9%); however, the situation was reversed in Rajasthan where it increased by 14%. Adaptation strategies were proposed including increased water allocation to Rajasthan and converting lands to cultivating more staple crops in Punjab and Haryana.

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“…Once the transition probability matrix is known, then projection for land use in the next state is estimated using Eq. 2b (Hamad et al 2018;Liping et al 2018;Marko et al 2016):…”

Section: Projected Land Use Change With Markov Chain Analysismentioning

confidence: 99%

Adaptation by Himalayan Water Resource System under a Sustainable Socioeconomic Pathway in a High-Emission Context

2021

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“…The projection of land use changes was performed with the spatial model coupling Markov Chains proposed by (Marko et al, 2016). The Markov Chain model contains two main processes: the transition probability matrix (τ) describes the probability of land cover changes from one period to another (Eq.…”

Section: Land Use Change Projectionmentioning

confidence: 99%

Assessment of Potential Impacts of Climate and Socio-Economic Changes on Future Water Security in the Himalayas, India

Dau¹,

Kuntiyawichai²

2021

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The shifts in socio-economic development and climate conditions currently become the challenge for water resources system security in the Himalayan region. The aforesaid concern was found pertinent to the main objective of this study, which is to evaluate the combined impacts of climate and socioeconomic changes on likely future water security in the Himalayan basin, India. The future climate was projected by Multi-model Ensembles under the Representative Concentration Pathway (RCP) 4.5 scenario. Land use projection under the Shared Socioeconomic Pathway (SSP) 1 scenario was performed using Markov Chain, whose transition probabilities were derived using multi-layer perceptron neural networks. The results showed that future annual precipitation and temperature at the downstream part will increase from baseline by 5% -10% and 1.0 o C -1.55 o C, respectively. The land use projections showed that irrigated areas will decrease for Punjab by 10% -30% and Haryana by 5% -10% due to the increase in urbanisation, whereas it will be increased in Rajasthan by 12% -18%. Consequently, the annual irrigation water demand was found to be decreased by 10% for Punjab and 5% for Haryana, while it will be increased by 13% for Rajasthan. Eventually, the obtained findings will be beneficial for planning strategies to ensure water security in the Himalayan region, in particular the Beas-Sutlej basin.

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“…Kadri et al (2008) menyatakan bahwa bagian hulu dari DAS Bekasi mengalami perubahan yang cepat sampai dengan tahun 2008 dengan peningkatan luasan permukiman (lahan terbangun) dari semula sebesar 4,39% menjadi 23,6% dari luas DAS. Penelitian sebelumnya oleh Marko et al (2016) yang memprediksi perubahan tutupan lahan di Daerah Aliran Ci Leungsi Hulu. Hasilnya, pada tahun 2030 diperkirakan terjadi peningkatan lahan terbangun sebesar hampir 100%.…”

Section: Pendahuluanunclassified

“…Nilai CN yang dihitung pada pemodelan debit banjir ini adalah CN rata-rata keseluruhan DAS. Pendekatan yang diterapkan adalah lumped model (Carpenter, 2006) (Marko et al, 2016). Secara spasial nilai CN yang ada di dalam DA Ci Leungsi Hulu dapat ditampilkan pada Gambar 5-a untuk kondisi yang aktual yakni pada tahun 2005, 2010, dan 2014, sedangkan untuk kondisi yang diprediksi yakni tahun 2020, 2025, dan 2030 dapat terlihat pada Gambar 5-b.…”

Section: Metodologi Penelitianunclassified

“…Penentuan Nilai Curve NumberNilai CN ditentukan dari hasil pertampalan kelompok tanah hidrologis atau hydrologic soil group (HSG) dan tutupan lahan berdasarkan standar yang telah ditetapkan oleh USDA (1986). Dalam penelitianMarko et al (2016) dihasilkan tutupan lahan tahun 2005, 2010, dan 2014 dan hasil prediksinya pada tahun 20120, 2025, dan 2030. Berdasarkan Gambar 3 terlihat bahwa lahan terbangun meningkat cukup signifikan.…”

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Pemodelan debit banjir sehubungan dengan prediksi perubahan tutupan lahan di daerah aliran Ci Leungsi Hulu menggunakan HEC-HMS

Marko¹,

Zulkarnain²

2018

jglitrop

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DAS Cileungsi Hulu adalah salah satu DAS yang memiliki peran penting dalam menyumbang limpasan air ke daerah hilir, yakni Kota Bekasi. Pertumbuhan penduduk yang tergolong tinggi telah mendorong perubahan pemanfaatan lahan secara signifikan. Salah satu penyebab banjir yang terjadi di Kota Bekasi adalah karena meningkatnya debit sungai di hulu Kali Bekasi. Penelitian ini bertujuan untuk mengestimasi debit banjir yang akan terjadi sehubungan dengan prediksi perubahan tutupan lahan pada tahun 2020, 2025, dan 2030. Metode SCS CN digunakan untuk perhitungan volume limpasan dan hidrograf satuan sintetis dengan menggunakan HEC-HMS. Nilai CN yang diperoleh pada data eksisting yakni 72,5; 74,4; dan 75,4 pada tahun 2005, 2010, dan 2014 secara berurutan, sedangkan hasil prediksi nilai CN pada tahun 2020, 2025, dan 2030 mengalami peningkatan yakni 77,2; 78,4; dan 79,4 secara berurutan. Penelitian ini menyimpulkan prediksi debit banjir pada tahun 2020, 2025, dan 2030 mengalami peningkatan, yakni dari 2020 ke 2025 sebesar 28,4%, dan dari 2025 ke 2030 sebesar 26.8%. Untuk periode ulang 25 tahun dengan curah hujan rancangan sebesar 197mm menghasilkan debit banjir sebesar 624,6; 653,0; dan 679,8 m3/dt pada tahun 2020, 2025, dan 2030 secara berturut-turut. Penelitian ini dapat bermanfaat bagi perencanaan pembangunan wilayah perkotaan, khususnya dalam mengantisipasi kerugian akibat adanya banjir di masa mendatang.Kata kunci: Pemodelan banjir, perubahan tutupan lahan, SCS-CN, HEC-HMS

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Coupling of Markov chains and cellular automata spatial models to predict land cover changes (case study: upper Ci Leungsi catchment area)

Cited by 18 publications

References 14 publications

Adaptation by Himalayan Water Resource System under a Sustainable Socioeconomic Pathway in a High-Emission Context

Adaptation by Himalayan Water Resource System under a Sustainable Socioeconomic Pathway in a High-Emission Context

Assessment of Potential Impacts of Climate and Socio-Economic Changes on Future Water Security in the Himalayas, India

Pemodelan debit banjir sehubungan dengan prediksi perubahan tutupan lahan di daerah aliran Ci Leungsi Hulu menggunakan HEC-HMS

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