Dynamics System Model for the Optimization of Irrigation Water Allocation
Abstract
This paper intends to build the optimization of irrigation water allocation. The research method uses a dynamics system that the methodology consists of building the causal loop diagram that amplifies (positive feedback loop) or negates (negative feedback loop) for a change in a system variable. Water balance is an important aspect of using the water resources. Its existence can provide an overview of the balance sheet conditions between water availability and water needs over a specific period. The water balance sheet can show the status of water availability conditions, whether the status is already in deficit or still excessive. Irrigation as part of water resource management is a system in society that is dynamic and depends on the environmental conditions, mainly because of the variety of actors, the variety of uses, across the different administrative areas and different cultures. This condition is further aggravated by the reduction in water sources that can be used as the main source of irrigation due to climate change, changes in land use and other uses such as domestic needs. Therefore, the complexity and limitations of the water resource are a challenge for irrigation development now and in the future. This research intends to optimize the irrigation water allocation by suing the dynamics system. By analyzing the causal relationships that affect the water availability, causal relationships that affect water demand, and formulating interventions with high leverage, the optimal condition is found. The results show that the limited amount of water availability with the increasing number of waters needs for various uses requires arranging the optimal water allocation in a fair, efficient and environmentally sound sense. However, the use of water for irrigation that can be allocated to fisheries will obtain maximum economic benefits if fisheries get top priority in water allocation.
Keywords: dynamic system model, irrigation, water allocation, optimization.
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ASMELITA, LIMANTARA L. M., BISRI M., SOETOPO W., and FARNI I. Allocation of existing water irrigation in Panti Rao. Journal of Southwest Jiaotong University, 2022, 57(3): 355-361. https://www.jsju.org/index.php/journal/article/view/1255/1245
HIDAYAT F., RASYAD A., ZULKARNAIN, BUSTAMI A. L., and LIMANTARA L. M. Dependable discharge of Molek irrigation water requirement related to the participation perspective of irrigation management. Journal of Southwest Jiaotong University, 2022, 57(3): 300-310. https://www.jsju.org/index.php/journal/article/view/1250
JUWONO P. T., LIMANTARA L. M., SOETOPO W., and NOPEBRIAN A. Optimization of irrigation cropping pattern (Case study on Karanganyar irrigation area, Malang regency, Indonesia. International Journal of Geomate, 2018, 15(50): 197-204. http://dx.doi.org/10.21660/2018.50.92322
BACHTIAR S., LIMANTARA L.M., SHOLICHIN M., and SOETOPO W. Assessment of water availability in the cascade reservoir of Duriangkang-Muka Kuning for supporting the integrated optimization. Journal of Southwest Jiaotong University, 2022, 57(3): 326-335. https://www.jsju.org/index.php/journal/article/view/1252/1242
OSAMA S., ELKHOLY M., and KANSOH R. M. Optimization of the cropping pattern in Egypt. Alexandria Engineering Journal, 2017, 56(4): 557-566. https://doi.org/10.1016/j.aej.2017.04.015
DOLGUI, A. & IVANOV, D. Ripple effect and supply chain disruption management: new trends and research directions. International Journal of Production Research, 2021, 59(1): 102–109. https://doi.org/10.1080/00207543.2021.1840148
FRAZZON E. M., FREITAG M., and IVANOV D. Intelligent methods and systems for decision-making support: toward digital supply chain twins. International Journal of Information Management, 2021, 57: 102281. http://dx.doi.org/10.1016/j.ijinfomgt.2020.102281
WANG F., CHEN Y., LI Z., FANG G., LI Y., and XIA Z. Assessment of the Irrigation Water Requirement and Water Supply Risk in the Tarim River Basin, Northwest China. Sustainability, 2019, 11(18): 4941. https://doi.org/10.3390/su11184941
DING Z., GONG W., LI S., and WU Z. Dynamics system versus agent-based modeling: A review of complexity simulation in construction waste management. Sustainability, 2018, 10(7): 2484. https://doi.org/10.3390/su10072484
DOLGUI A., IVANOV D., and ROZHKOV M. Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, 2020, 58(5): 1285–1301. https://doi.org/10.1080/00207543.2019.1627438
AGUILA O. J. and ELMARAGHY W. Dynamics system modelling for supply chain disruptions. International Journal of Production Research, 2020, 59(6): 1757-1775. https://doi.org/10.1080/00207543.2020.1725171
RATHORE R., THAKKAR J., and JHA J. K. Impact of risks in food grains transportation system: A Dynamics system approach. International Journal of Production Research, 2020, 59(6): 1814-1833. https://doi.org/10.1080/00207543.2020.1725683
PRODANOVIC P., & SIMONOVIC S. P. An Operational Model for Support of Integrated Watershed Management. Water Resources management, 2010, 24: 1161-1194. https://doi.org/10.1007/s11269-009-9490-6
BADAN PUSAT STATISTIK. Statistik Indonesia 2017, 2017. https://www.bps.go.id/publication/2017/07/26/b598fa587f5112432533a656/statistik-indonesia-2017.html
KEMENTERIAN KELAUTAN DAN PERIKANAN. Ministry of Marine Affairs and Fisheries Republic of Indonesia, 2019. http://www.kkp.go.id/en/
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