Comparative analysis of gridded and station-based meteorological data for deep learning-based streamflow prediction

Accurate streamflow prediction is fundamental for water resource management and disaster response. However, predicting streamflow with station-based meteorological observations faces challenges due to low spatial density. In contrast, gridded meteorological data provide spatially continuous information, leading to improved streamflow prediction accuracy. Deep learning (DL) models have been widely adopted in water management and mostly use precipitation as an input. Therefore, this study tests whether gridded precipitation improves the predictive accuracy of DL models for streamflow in the Miho River Watershed, South Korea. Modified Korean Parameter-elevation Regression on Independent Slopes Model (MK-PRISM) is used as gridded precipitation. The MK-PRISM data with 1 km spatial resolution consider elevation, topographic facet, and coastal proximity. This study utilizes six meteorological variables: precipitation, average temperature, maximum temperature, minimum temperature, wind speed, and relative humidity. Three DL models, Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Networks-LSTM (CNN-LSTM), are used in this study. Five experimental cases are developed for this study. Cases 1 through 4 utilize LSTM and Bi-LSTM, while Case 5 implements a CNN-LSTM. Case 1 uses station-averaged data across the watershed. Case 2 employs the average of MK-PRISM at the watershed level. Case 3 uses meteorological data from individual stations. Case 4 utilizes the average of MK-PRISM at the sub-basin level. Finally, Case 5 employs a CNN-LSTM to use the original format of MK-PRISM as input data. The results of this study will demonstrate the advantages of gridded precipitation to predict streamflow with DL models and propose a suitable format of gridded precipitation.