TITLE:
A Framework to Regionalize Flow Information in a Catchment with Limited Hydrological Data
AUTHORS:
Misigo W. S. Angalika, Seiji Suzuki, Wataru Tanaka, Huynh V. Vu, Tomoaki Itayama
KEYWORDS:
Prediction, Uncertainty, Regionalization, Mapping, Transplanting Flow Signatures
JOURNAL NAME:
Open Journal of Modern Hydrology,
Vol.13 No.1,
January
11,
2023
ABSTRACT: This paper describes a framework for mapping flow information from a
single gauge to the 9-ungauged river basins with distinct attributes. To
establish the basic watershed characteristics at the gauged site, a hydrologic
model was calibrated and validated against the historical continuous discharge
dataset. The framework was then applied to account for the two watersheds’
proportionality in their similarity, such as the influence of land use on
transplanting flow signatures to the ungauged site. Three land-use
scenarios-discharges at the ungauged and gauged sites formed the basis of an
equation mapping the gauged discharge signal to the ungauged site. In
comparison with intermittent observed data, the framework prediction attained a
precision of 0.85 ≥ NSE ≤ 0.95, 0.80 ≥ R2 ≤ 0.94, 0.56 ≥ bR2 ≤ 0.89.
Despite considerable differences in the watershed area, slope, soils, and land
cover, the framework satisfactorily depicted the variation in flow pulses at
each of the 9 ungauged discharge sites. In the absence of sufficient
hydrological information, for example, the presence of a single gauge, the
framework provides an alternative method to estimate flow at ungauged sites,
reducing uncertainties in the regionalization of model parameters.