Deep‐learning based projection of change in irrigation water‐use under RCP 8.5

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. This study proposed a deep‐learning based model to estimate agricultural water withdrawal using hydro‐meteorological variables. The relationships between meteorological variables and stream water use rate (WUR) were quantified using a deep belief network (DBN). 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 and thus leads to increase WUR estimated by DBN during the future period.