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\begin{document}

\title{Dynamic Data Assimilation for Improved Streamflow Forecast Using
Sensitive Soil Moisture Observations}



\author[1]{R Visweshwaran}%
\author[1]{Raaj Ramsankaran}%
\author[1]{TI Eldho}%
\author[2]{S. Lakshmivarahan}%
\affil[1]{Indian Institute of Technology Bombay}%
\affil[2]{National Severe Storms Lab, Desert Research Inst,}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
The accuracy of streamflow forecasts is important for efficient
monitoring and mitigation of flood events. Unfortunately, the
uncertainty in the model control variable which includes model
parameters, initial and boundary conditions, propagates through the
model, resulting in the degradation of streamflow forecast. Various
studies in the past have shown the potential of soil moisture
assimilation in hydrological models resulting in the improved forecast.
Further, the efficiency of assimilation is based on the number and the
distribution of observations used. This study proposes a new approach
called Forward sensitivity method (FSM), which operates in two phases.
By running the model and forecast sensitivity dynamics forward in time,
the first phase places the observations at or near where the square of
the forecast sensitivity with respect to the control takes maximum
values. Then using only this subset of observations, the second phase
estimates the unknown elements of the control by solving a resulting
weighted least squares problem. The power of this approach is
demonstrated by assimilating ASCAT soil moisture observations into a
conceptual Two Parameter Model in a medium sized watershed lying in the
Krishna River Basin, India. The model run extends for four monsoon years
from June 2007 to June 2011 and two assimilation scenarios were tested.
The first scenario uses all the observations, whereas, the second uses
only sensitive observations during assimilation and the results were
then compared against open loop simulation (model run without
assimilation). Sensitivity results indicate that observations during
monsoon time alone are sufficient for assimilation purpose, which
accounts for only 37.42 percent of total observations. Also, the
estimation and forecast results show improved streamflow performance
when using only sensitive observations. From the results, it is
concluded that FSM based assimilation can help in reducing the
computation time greatly. Further, this study will be critically helpful
in the places where data availability remains a major problem.%
\end{abstract}%



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