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\begin{document}

\title{Analysing the Capability of the Catchment's Spectral Signature for the
Regionalization of Hydrological Parameters}



\author[1]{Laura Fragoso-Campón}%
\author[1]{Pablo Durán-Barroso}%
\author[2]{Elia Rosado}%
\affil[1]{Universidad de Extremadura}%
\affil[2]{Universidad de Extremadura Escuela Politecnica}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Water resource management in ungauged catchments is complex due to the
uncertainties around the hydrological parameters that dominate the
streamflow behaviour. These parameters are usually defined by
regionalization approaches in which hydrological response patterns are
transferred from gauged to ungauged basins. Regression-based methods
using physical properties derived from cartographic data sources are
widely used. The current remote sensing techniques offer us new
standpoints in regionalisation processing since the hydrological
response depends on the physical attributes related to the spectral
responses of the territory. Moreover, machine learning approaches have
not been specifically applied to the regionalization of hydrologic
parameters. This work studies the capability of a catchment's spectral
response based on Sentinel-1 and Sentinel-2 data to address a
regression-based regionalization of hydrological parameters using a
machine learning approach. Hydrological modelling was conducted by the
HBV-light model. We tested the random forest algorithm in several
regionalization scenarios: the new approach using the catchments'
spectral signature, the traditional method using physical properties and
a fusion of them. The calibration results were excellent (median KGE =
0.83), and the regionalized parameters obtained with the random forest
algorithm achieved good performance in which the three scenarios showed
almost the same goodness of fit (median KGE = 0.45 to 0.50). We found
that the effectiveness depends on the climatic environment and that
predictions in humid catchments exhibited better performance than those
in the driest catchments. The physical approach (median KGE= 0.71)
exhibited better performance than the spectral approach (median KGE=
0.64) in humid catchments, whereas spectral regionalization (median KGE=
0.33) outperformed the physical scenario in the driest catchments
(median KGE= 0.25). Herein, our results confirm that regionalization is
still challenging in Mediterranean climate variants where the new
spectral approach showed promising results and time series of satellite
data could improve seasonal regionalization methodologies.%
\end{abstract}%



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