Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements

Abstract

Soil temperature is one of the most important glacio-meteorological parameters that play a critical role in glacier energy and mass balance dynamics, surface hydrological processes, and glacier-atmosphere interaction. However, the availability of the data is acutely scarce in the Himalayan glaciated region. In this study, we applied artificial neural network (ANN) models for the prediction of soil temperature of glacial forefield region of the Pindari Glacier (Central Himalaya). Three-layer feed-forward ANN models were developed and tested for estimating multi-depth soil temperatures using concurrent and antecedent air-soil temperature data for one complete annual cycle as inputs for the models. Models with different combinations of input variables were tested, and best sets of variables were selected based on the prediction accuracy. Rigorous statistics were further employed to compare the performances of different models. High concurrence was obtained between ANN-estimated and measured soil and air temperatures as evident by various correlation coefficients and error ranges. In a boarder perspective, our results point toward the applicability of developed ANN models to provide robust soil temperature prediction for the glacial forefield regions of the Central Himalaya.