Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-525-2026
https://doi.org/10.5194/hess-30-525-2026
Research article
 | 
02 Feb 2026
Research article |  | 02 Feb 2026

Joint calibration of multi-scale hydrological data sets using probabilistic water balance data fusion: methodology and application to the irrigated Hindon River Basin, India

Roya Mourad, Gerrit Schoups, Vinnarasi Rajendran, and Wim Bastiaanssen

Viewed

Total article views: 1,755 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,531 188 36 1,755 44 34
  • HTML: 1,531
  • PDF: 188
  • XML: 36
  • Total: 1,755
  • BibTeX: 44
  • EndNote: 34
Views and downloads (calculated since 04 Aug 2025)
Cumulative views and downloads (calculated since 04 Aug 2025)

Viewed (geographical distribution)

Total article views: 1,755 (including HTML, PDF, and XML) Thereof 1,608 with geography defined and 147 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Feb 2026
Download
Short summary
Water balance data are affected by various errors (bias and noise). To reduce these errors, this study presents a water balance data fusion approach that combines multi-scale data (from satellites and in-situ sensors) for each water balance variable and jointly calibrates them, resulting in consistent, bias-corrected and noise-filtered, water balance estimates, along with uncertainty bands. These estimates are useful for constraining process-based models and informing water management decisions.
Share