Abstract
As research on the use of satellites in combination with previous hydrological monitoring techniques increases, interest in the application of the machine-learning approach to the prediction of hydrological variables is growing. Ground-based measurements are often limited due to the difficulties in measuring spatiotemporal variations, especially in ungauged areas. In addition, there are no existing satellites capable of measuring total discharge directly. In this study, Artificial neural network (ANN) machine-learning approaches are examined for the prediction of 0.25° total discharge data over the Korean Peninsula using the data fusion of multi-satellites, reanalysis data, and ground-based observations. Terrestrial water storage changes (TWSC) of the Gravity Recovery and Climate Experiment (GRACE) satellite, precipitation of the tropical rainfall measuring mission (TRMM), and soil moisture storage and average temperature of the global land data assimilation system (GLDAS) models are used as ANN model input data. The results demonstrate the relatively good performance of the ANN approach for predicting the total discharge in terms of the correlation coefficient (r = 0.65–0.95), maximum absolute error (MAE = 13.28–20.35 mm/month), root mean square error (RMSE = 22.56–34.77 mm/month), and Nash-Sutcliff efficiency (NSE = 0.42–0.90). The precipitation is identified as the most influential input parameter through a sensitivity analysis. Overall, the ANN-predicted total discharge shows similar spatial patterns to those from other methods, while GLDAS underestimates the total discharge with a smaller dynamic range than the other models. Thus, the potential of the ANN approach described herein shows promise for predicting the total discharge based on the data fusion of multi-satellites, reanalysis data, and ground-based observations.
Highlights
Total discharge, which includes surface and subsurface discharge, is an elementary component of the hydrological cycle over the Earth
The target variables were the total discharge from the global land data assimilation system (GLDAS) Noah model for the Korean Peninsula and the enhanced total discharge data obtained by combining the GLDAS and discharge gauge station data for South Korea (SK) via the conditional merging method
The results of Artificial neural network (ANN) modelling to predict the downscaled total discharge using the various satellite and reanalysis products are presented in Figure 6, where each row represents a specific case, and each column represents the results of each period
Summary
Total discharge, which includes surface and subsurface discharge, is an elementary component of the hydrological cycle over the Earth. Continuous total discharge data are vital for the monitoring of extreme hydrological events, such as droughts and floods, and for water management. Dependable total discharge predictions can allow the water usage efficiency to increase and agricultural and economic losses to be minimized [1,2]. The monitoring and prediction of the discharge calls for continuous and reliable historical hydrometeorological/hydraulic data (i.e., precipitation, humidity, temperature, flow velocity, slope, etc.), which are typically applied to complex physical modelling methods. The quality of the station measurement networks is restricted in many mountainous parts of the world, where basic hydrologic data is insufficient and sparse and sites are inaccessible areas [3]. Physical models based on these data are difficult to set up, because the data collection, archiving, and distribution have suffered from the inadequate operation and maintenance of facilities, especially in ungauged areas
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
