Characteristics of hydrological loading signals in GNSS observations: a case study from IITK and LCK4 stations


Ray J. D., ANSARI K., Godah W.

Journal of Applied Geodesy, 2026 (ESCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1515/jag-2025-0101
  • Dergi Adı: Journal of Applied Geodesy
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Compendex, Geobase, INSPEC
  • Anahtar Kelimeler: Earth’s surface deformation, GNSS, hydrological loading, land surface discharge model, wavelet analysis
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The study investigates surface deformation caused by hydrological mass changes using Global Navigation Satellite System (GNSS) data and a hydrological model at two International GNSS Service (IGS) stations-IIT Kanpur (IITK) and Lucknow (LCK4)-located in the Ganges basin. A key novelty of this work is the comparative assessment of GNSS-observed and Land Surface Discharge Model (LSDM)-modeled vertical Earth surface deformation over North India. The goal is to assess how variations in terrestrial water storage affect vertical crustal deformation in this region. GNSS-derived vertical displacement time series were compared with those modeled using the Land Surface Discharge Model (LSDM), that provides terrestrial water storage changes at a 0.5° × 0.5° spatial resolution. Both GNSS and LSDM time series exhibit a clear annual cycle, reflecting the dominant impact of seasonal hydrological loading. To analyze the relationship between GNSS-observed and LSDM-modeled deformations, wavelet transform analysis was employed to detect time-localized spectral power and coherence across frequencies between the two datasets. Correlation coefficients between GNSS and LSDM deformation exceed 0.7 at both IGS stations, indicating strong agreement. However, LSDM explains only about 40–60 % of the variance observed in the GNSS data, suggesting the influence of other geophysical or environmental factors are not captured by the model. Wavelet coherence analysis confirms strong correlation in the annual frequency band.