Study on spatio-temporal simulation and prediction of regional deep soil moisture using machine learning

Yinglan A; Xiaoman Jiang; Yuntao Wang; Libo Wang; Zihao Zhang; Limin Duan; Qingqing Fang

doi:10.1016/j.jconhyd.2023.104235

Study on spatio-temporal simulation and prediction of regional deep soil moisture using machine learning

J Contam Hydrol. 2023 Sep:258:104235. doi: 10.1016/j.jconhyd.2023.104235. Epub 2023 Aug 23.

Authors

Yinglan A¹, Xiaoman Jiang¹, Yuntao Wang², Libo Wang¹, Zihao Zhang¹, Limin Duan³, Qingqing Fang⁴

Affiliations

¹ State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
² State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. Electronic address: ytwang@bnu.edu.cn.
³ Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China.
⁴ School of Water Conservancy and Hydropower Engineering, North China Electric Power University, Beijing 102206, China.

PMID: 37651919
DOI: 10.1016/j.jconhyd.2023.104235

Abstract

Deep soil moisture (SM) plays a crucial role in vegetation restoration, particularly in semi-arid areas. However, current SM products have limited access and do not meet the spatio-temporal scale and soil depth requirements in eco-hydrological research. Thus, this study constructs a random forest prediction model for SM at different depths by identifying driving factors and quantifying the correlation effect of vertical SM based on the international SM network dataset. Subsequently, the SMAP product is integrated into the model to expand SM from point scale to regional scale, yielding an SM data product with a suitable scale and continuous time and space. The results indicate that the correlation between precipitation and SM changes into the interaction between adjacent SM layers as the depth increases. The lag time of SM in the shallow surface layer (0-3 cm) to precipitation was 1 day, and there was no delay on the daily scale in the 3-20 cm layers of the three underlying surface types. The response time of 50 cm SM to 20 cm SM was 1-2 days in cropland and grassland and 2 days in forest. Slope, land use type, clay proportion, leaf area index, potential evapotranspiration, and land surface temperature were the key driving factors of SM in the Shandian River region. The random forest model established in this study demonstrated good prediction performance for SM at both site and regional scales. The obtained daily products had higher spatial fineness than CLDAS products and could describe the SM characteristics of different underlying surfaces. This study offers new ideas and technical support for acquiring deep SM data in arid and semi-arid areas of northern China.

Keywords: Hysteresis effect; Random forest; SMAP data; Semi-arid areas; Soil moisture.