The application of artificial neural networks to simulate meltwater runoff of Keqikaer Glacier, south slope of Mt. Tuomuer, western China

Abstract

Because glacial melting provides a significant amount of surface water resources, especially in cold arid regions, it is critical that effective methods be developed for predicting their behavior. Glacier runoff differs from other types of stream flows, being characterized by large diurnal fluctuations, with maximum discharge during the summer months. Moreover, the size and remoteness of glaciers makes them difficult to study directly. Hence, developing effective modeling techniques is our best hope for understanding and predicting glacial melting phenomena. In the past, physics-based models have been used with some success. In this study, conducted in 2003 and 2004 on the Keqikaer Glacier on the south slope of Mt. Tuomuer, however, we used the newer artificial neural networks (ANNs) modeling technique. As the input nerve cell, we used the hourly wind speed, precipitation, air temperature, radiation balance, and ground temperature; the output nerve cell was the diurnal runoff at the glacial terminus. We then analyzed the simulated results under different scenarios by varying the input-nerve-cell parameters. It was found that ANN can simulate the process of glacier meltwater runoff successfully when basic parameters such as air temperature, precipitation and radiation balance are few. The results indicate that ANN can simulate the process of glacial meltwater runoff quite well, and that meteorological variables could in fact be used successfully to simulate glacier meltwater runoff using the ANN method.