Zhang et al. (2025) Characterizing snow droughts and deluges in the Sacramento River Basin, California using GNSS-derived snow depth (2009–2023)

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-10-07
• Authors: Yinghong Zhang, M.-C. Chang, Zhongshan Jiang, Rumeng Guo, Xingyuan Yan, Wei Feng, Min Zhong
• DOI: 10.1016/j.jhydrol.2025.134336

Research Groups

• School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
• State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan 430077, China
• Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, China

Short Summary

This study retrieves daily snow depth using GNSS Interferometric Reflectometry (GNSS-IR) at four stations in the Sacramento River Basin (2009–2023) to characterize snow droughts and deluges, revealing that 57.1 % of snow drought events exhibited compound characteristics driven by both precipitation and temperature anomalies.

Objective

• To retrieve daily snow depth time series using GNSS Interferometric Reflectometry (GNSS-IR) and systematically analyze snow drought and deluge characteristics (spatiotemporal patterns, classification, and evolutionary dynamics) in the Sacramento River Basin, California.

Study Configuration

• Spatial Scale: Sacramento River Basin, California, USA (at four GNSS stations).
• Temporal Scale: Water Years (WYs) 2009–2023.

Methodology and Data

• Models used: GNSS Interferometric Reflectometry (GNSS-IR) for snow depth retrieval; Standardized Snow Depth Index (SSDI) for characterizing snow droughts and deluges.
• Data sources: GNSS-IR derived snow depth; nearby ground-based snow depth data for validation; meteorological data (precipitation, temperature) for integrated analysis of drought types.

Main Results

• GNSS-IR provides centimeter-level accuracy for snow depth, validated with a Mean Absolute Error (MAE) of 8.3 cm and a Root Mean Square Error (RMSE) of 13.8 cm.
• Major snow drought events, lasting over four months, were concentrated during Water Years (WYs) 2012–2016 and 2018.
• Snow deluge periods occurred in four distinct episodes: WYs 2010, 2011, 2019, and 2023, with WY 2017 classified as a moderate snow deluge.
• Integrated analysis with meteorological data revealed that 57.1 % of snow drought events displayed compound characteristics, combining features of both dry snow drought (DSD, precipitation-driven) and warm snow drought (WSD, temperature-driven).
• DSDs lead to immediate and persistent water deficits, while WSDs, despite temporary alleviation of water shortages, accelerate snowmelt due to higher winter temperatures, thereby intensifying spring-summer water scarcity.

Contributions

• Confirms the effectiveness of GNSS-IR for high-temporal-resolution snow depth monitoring, providing a valuable tool for hydrological applications.
• Offers a systematic and detailed characterization of snow drought and deluge events, including their spatiotemporal patterns, classification, and evolutionary dynamics, in a critical snow-dependent basin.
• Highlights the prevalence and distinct impacts of compound snow drought events (dry and warm) on water resources.
• Provides technical support for hydrological early warning systems and adaptive water resource management strategies in snow-dependent watersheds.

Funding

• Not specified in the provided text.

Citation

@article{Zhang2025Characterizing,
  author = {Zhang, Hui and Zhang, Yinghong and Chang, M.-C. and Jiang, Zhongshan and Guo, Rumeng and Yan, Xingyuan and Feng, Wei and Zhong, Min},
  title = {Characterizing snow droughts and deluges in the Sacramento River Basin, California using GNSS-derived snow depth (2009–2023)},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134336},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134336}
}