Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Aliva Nanda,K P Sudheer,Awshesh Nath Sharma,Sumit Sen

doi:10.3390/w12030713

Aliva Nanda, K P Sudheer + Show 2 more

Open Access

https://doi.org/10.3390/w12030713

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Abstract

Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.

Highlights

Soil temperature governs the terrestrial ecosystem processes and the exchange of carbon, moisture, and energy in the land–atmosphere nexus [1]
The graph shows that the daily average soil temperature of the GA hillslope is more than the AgF hillslope as the south aspect hillslope (GA) receives more solar radiation than the north aspect (Figure 3)
The soil moisture started falling from the mid of September and the lowest spread is observed during colder months (i.e., November to February)

Summary

Introduction

Soil temperature governs the terrestrial ecosystem processes and the exchange of carbon, moisture, and energy in the land–atmosphere nexus [1]. The rate and type of chemical, physical, and biological actions of the ecosystem are linked with soil temperature [2]. Soil temperature influences multiple environmental processes such as soil respiration, infiltration, evaporation, nutrient uptake, accumulation and degradation of soil organic matters, root growth, microorganism growth, and plant growth. The variation in soil temperature stimulates botanical biodiversity. Water 2020, 12, 713 flux estimation, crop simulation, and meteorological modeling require fine-resolution observed soil temperature datasets [3]. Similar to the Alpine region, hydrological, ecological, and meteorological processes of the Indian Himalayan Region (IHR) are sensitive to soil temperature

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