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Title: Soil temperature dynamics at hillslope scale-field observation and machine learning-based approach
Authors: Nanda A.
Sen, Sumit
Sharma A.N.
Sudheer K.P.
Published in: Water (Switzerland)
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. © 2020 by the authors.
Citation: Water (Switzerland) (2020), 12(3): -
Issue Date: 2020
Publisher: MDPI AG
Keywords: Data-scarce region
Hillslope hydrology
Lesser himalayan hillslopes
Machine learning
Soil temperature
ISSN: 20734441
Author Scopus IDs: 57201619450
Author Affiliations: Nanda, A., Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Sen, S., Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Sharma, A.N., Integrated M. Tech Geological Technology, Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Sudheer, K.P., Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, 600036, India
Funding Details: Funding: Authors would like to acknowledge the Science & Engineering Research Board (SERB), Department of Science and Technology (DST) under grant # SER-776 towards field visits and instrumentation. The authors are also grateful to editorial committee of Water, MDPI for providing 100% discount on Article Processing Charge (ACP).;Acknowledgments: The authors are grateful to all the members of the research group (especially Vikram Kumar and Ravi Meena) for their support during installation. Moreover, the authors are thankful to all the local field persons of Aglar watershed. The first author would like to thank the Ministry of Human Resource Development (MHRD), India for providing fellowship during the PhD program. The third author would like to thank the IIT Roorkee for providing SPARK summer internship to work under this project. Finally, authors would like to thank the anonymous reviewers for providing constructive comments and suggestions.
Corresponding Author: Sen, S.; Department of Hydrology, Indian Institute of Technology RoorkeeIndia; email:
Appears in Collections:Journal Publications [HY]

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