• Korean Journal of Soil Science and Fertilizer
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• v.38 no.4
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• pp.222-229
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• 2005

A simplified one-dimensional model STELLA was used to predict soil water movement in lllinois corn fields using soil water balance sheets. It offered the potential to increase understanding of soil nitrate and agrochemical leaching process. The model accounted for aU possible annual inputs and outputs of water from a closed ecosystem as represented by corn fields. Water inputs included precipitation, while outputs included runoff, transpiration, evaporation and drainage. To run the model required daily inputs of two climatic data measurements such as daily precipitation and pan evaporation. Vertical water flow through the soil profile was calculated with first order equation including the difference in hydraulic conductivity and matric potential at the various soil types. The output results included daily changes of water content in the soil layers and daily amount of water losses including run-off, percolation, transpiration. This model was verified using Illinois corn field data for the soil water content measured by neutron scattering methods through 1992 to 1994 growing seasons. Approximately 22 to 78% of simulated water contents agreed with the measured values and their standard deviation, depending on soil types, whereas 30 to 70% of simulated water values agreed with the measured values and their standard deviations depending on soil layers.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.