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<p>The study of surface hydrological processes and their time-lag relationships with atmospheric conditions is crucial for effective water resource management and drought early warning systems. To comprehensively understand these complex interactions, large-scale, continuous, and accurate hydrological cycle analyses are essential. This paper proposes an integrated approach for analyzing hydrological time-lag effects using multiple Global Navigation Satellite System (GNSS) technologies, including GNSS reflectometry (GNSS-R) soil moisture (SM) retrieval, GNSS positioning, and GNSS-based water vapor retrieval. GNSS-R SM retrieval, a relatively new technique, serves as the primary focus of this study. Using 2023 Cyclone GNSS (CYGNSS) data and the CatBoost machine learning model, we successfully retrieved SM data for the Southern United States and Central America. The second focus of this study is the hydrologic time-lag analysis (TLA) involving various types of GNSS data. Specifically, we examined the time-lag relationships among precipitable water vapor (PWV), GNSS-R SM, GNSS vertical displacement, and vegetation water content (VWC). The results demonstrate significant time-lag effects among these variables, with notable heterogeneity influenced by the vegetation cover type. In particular, GNSS vertical displacement lags PWV by approximately 2–4 months, SM lags PWV by approximately 0–20 days, VWC lags PWV by approximately 0–80 days, and VWC lags SM by approximately 0–30 days. These findings indicate that the combined various GNSS techniques can effectively analyze the time-lag relationships among hydrologic variables at relatively high spatial and temporal resolutions, offering new insights for understanding and predicting hydrologic dynamics under climate change.</p>

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Analysis of hydrological time-lag effects using multiple GNSS techniques: GNSS-R-retrieved soil moisture, GNSS-derived coordinates, and GNSS-based water vapor data

  • 期刊论文

  • 发表日期：

    2025年9月12日

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  • Weiao Yong,
  • Xiaolei Wang,
  • Jinsheng Tu,
  • Ying Gao

摘要

The study of surface hydrological processes and their time-lag relationships with atmospheric conditions is crucial for effective water resource management and drought early warning systems. To comprehensively understand these complex interactions, large-scale, continuous, and accurate hydrological cycle analyses are essential. This paper proposes an integrated approach for analyzing hydrological time-lag effects using multiple Global Navigation Satellite System (GNSS) technologies, including GNSS reflectometry (GNSS-R) soil moisture (SM) retrieval, GNSS positioning, and GNSS-based water vapor retrieval. GNSS-R SM retrieval, a relatively new technique, serves as the primary focus of this study. Using 2023 Cyclone GNSS (CYGNSS) data and the CatBoost machine learning model, we successfully retrieved SM data for the Southern United States and Central America. The second focus of this study is the hydrologic time-lag analysis (TLA) involving various types of GNSS data. Specifically, we examined the time-lag relationships among precipitable water vapor (PWV), GNSS-R SM, GNSS vertical displacement, and vegetation water content (VWC). The results demonstrate significant time-lag effects among these variables, with notable heterogeneity influenced by the vegetation cover type. In particular, GNSS vertical displacement lags PWV by approximately 2–4 months, SM lags PWV by approximately 0–20 days, VWC lags PWV by approximately 0–80 days, and VWC lags SM by approximately 0–30 days. These findings indicate that the combined various GNSS techniques can effectively analyze the time-lag relationships among hydrologic variables at relatively high spatial and temporal resolutions, offering new insights for understanding and predicting hydrologic dynamics under climate change.