Mon Feb 16, F. Flechtner
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Mourad, Roya, Schoups, Gerrit, Rajendran, Vinnarasi, and Bastiaanssen, Wim, 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. Hydrology and Earth System Sciences Discussions, 30(3):525–551, doi:10.5194/hess-30-525-2026.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026HESSD..30..525M,
author = {{Mourad}, Roya and {Schoups}, Gerrit and {Rajendran}, Vinnarasi and {Bastiaanssen}, Wim},
title = "{Joint calibration of multi-scale hydrological data sets using probabilistic water balance data fusion: methodology and application to the irrigated Hindon River Basin, India}",
journal = {Hydrology and Earth System Sciences Discussions},
year = 2026,
month = feb,
volume = {30},
number = {3},
pages = {525-551},
abstract = "{Hydrological data sets have vast potential for water resource management
applications; however, they are subject to uncertainties. In
this paper, we develop and apply a monthly probabilistic water
balance data fusion approach for automatic bias correction and
noise filtering of multi-scale hydrological data. The approach
first calibrates the independent data sets by linking them
through the water balance, resulting in hydrologically
consistent estimates of precipitation (P), evaporation (E),
storage (S), irrigation canal water imports (C), and river
discharge (Q) that jointly close the basin-scale water balance.
Next, the basin-scale results are downscaled to the pixel-scale,
to generate calibrated ensembles of gridded Precipitation (P)
and Evaporation (E) that reflect the basin-wide water balance
closure constraints. An application to the irrigated Hindon
River basin in India illustrates that the approach generates
physically reasonable estimates of all basin-scale variables,
with average standard errors decreasing in the following order:
21 mm month$^{{\ensuremath{-}}1}$ for storage, 10 mm
month$^{{\ensuremath{-}}1}$ for evaporation, 7 mm
month$^{{\ensuremath{-}}1}$ for precipitation, 4 mm
month$^{{\ensuremath{-}}1}$ for irrigation canal water imports,
and 2 mm month$^{{\ensuremath{-}}1}$ for river discharge.
Results show that updating the original independent data with
water balance constraint information reduces uncertainties by
inducing cross-correlations between all independent variables
linked through the water balance. In addition, the introduced
approach yields (i) hydrologically consistent gridded P and E
estimates that fuse information from prior (original) data
across different land use elements and (ii) statistically
consistent random errors that reflect the model's confidence
about P and E estimates at each grid cell. The analysis also
shows a long-term decreasing trend in groundwater, which is
better captured by the more severe decline from GRACE JPL mascon
than GRACE Spherical Harmonic data. This finding points towards
the possible sustainability issues for irrigation in the basin
and requires further validation using piezometer groundwater-
level measurements. Future opportunities exist to further
constrain the generated water balance variables and their
associated errors within process-based models and with
additional data.}",
doi = {10.5194/hess-30-525-2026},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026HESSD..30..525M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Feb 16, F. Flechtner