RESEARCH ARTICLE
Deep-learning based projection of change in irrigation water-use under RCP 8.5
Jang Hyun Sung,
Jinsoo Kim,
Eun-Sung Chung,
Young Ryu,
Jang Hyun Sung
Han River Flood Control Office, Ministry of Environment, Seoul, South Korea
Search for more papers by this authorJinsoo Kim
Land, Transport and Maritime Affairs Team, National Assembly Research Service, Seoul, South Korea
Search for more papers by this authorEun-Sung Chung
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul, South Korea
Search for more papers by this authorCorresponding Author
Young Ryu
Operational Systems Development Department, National Institute of Meteorological Research, Jeju, South Korea
Correspondence
Young Ryu, Operational Systems Development Department, National Institute of Meteorological Research, 33 Seohobuk-ro, Seogwipo, Jeju, 63568, South Korea.
Email: [email protected]
Search for more papers by this authorJang Hyun Sung,
Jinsoo Kim,
Eun-Sung Chung,
Young Ryu,
Jang Hyun Sung
Han River Flood Control Office, Ministry of Environment, Seoul, South Korea
Search for more papers by this authorJinsoo Kim
Land, Transport and Maritime Affairs Team, National Assembly Research Service, Seoul, South Korea
Search for more papers by this authorEun-Sung Chung
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul, South Korea
Search for more papers by this authorCorresponding Author
Young Ryu
Operational Systems Development Department, National Institute of Meteorological Research, Jeju, South Korea
Correspondence
Young Ryu, Operational Systems Development Department, National Institute of Meteorological Research, 33 Seohobuk-ro, Seogwipo, Jeju, 63568, South Korea.
Email: [email protected]
Search for more papers by this authorFunding information National Research Foundation of Korea, Grant/Award Number: 2021R1A2C20056990
Abstract
Stream water-use is essential for both agricultural and hydrological management and yet not many studies have explored its non-stationarity and nonlinearity with meteorological variables. This study proposed a deep-learning based model to estimate agricultural water withdrawal using hydro-meteorological variables, which projected the changes of agricultural water withdrawal influenced by climate change of future. The relationships between meteorological variables and stream water-use rate (WUR) were quantified using a deep belief network (DBN). The influences of precipitation, potential evapotranspiration, and monthly averaged WUR on the performance of the developed DBN model were tested. As a result, this DBN with potential evapotranspiration (PET) provided better performances than precipitation to estimate the WUR. The PET of multi-model scenarios for Representative Concentration Pathways 8.5 would be increased as time goes by, and thus leads to increase WUR estimated by DBN in three basins, located in South Korea during the future period. On the contrary, water availability expected to decrease compared to the current. Therefore, managing water-uses and improving efficiencies can be prepared for the change in agricultural water-use by climate change in the future.
Open Research
DATA AVAILABILITY STATEMENT
N/A.
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