Abstract:
Accurate streamflow simulation and comprehending its associated uncertainty are crucial
for effective water resource management. However, the uncertainty of rating curves from
which streamflow data is derived remains poorly understood. This study aims to simulate
streamflow at the outlet of the Kulfo watershed under rating curve uncertainty conditions.
To achieve this, the bootstrap resampling technique (BSRT) was employed to establish the
rating curve and estimate associated uncertainty. Further, it integrated with Recurrent
neural network (RNN) models, specifically long short-term memory (LSTM), bidirectional
long short-term memory (BiLSTM), and gated recurrent units (GRU) to assess the effect of
this uncertainty on streamflow simulation accuracy and reliability. Different sets of
streamflow data, derived from the fitted rating curve and its lower and upper uncertainty
bands were utilized to train RNN models independently, and used for scenario analysis.
Different Lag times of daily and monthly areal rainfall and discharge are used as model
inputs, and executed in Python programming language. By comparing the results from
different rating curve uncertainty scenarios, the study evaluates the effect of this
uncertainty on streamflow simulation. The findings reveal the excellent potential of RNN
models for streamflow simulation, with the BiLSTM model outperforming LSTM and GRU
at both temporal scales. Moreover, the study highlights that rating curve uncertainty
propagates significantly onto streamflow simulations, particularly in high-flow regions.
Consequently, the rating curve uncertainty in Kulfo River led to an uncertainty of the
streamflow of about 17.8 m3
s
-1 and 9.8 m3
s
-1
, representing about 25% and 22% at peak
discharge for daily and monthly simulations respectively. These findings underscore the
importance of considering rating curve uncertainty in streamflow simulation to ensure
accurate results. Thus, the simulated flow should not be directly used for decision-making
in the watershed. Instead, streamflow should be treated as an uncertain variable and
managed by incorporating rating curve uncertainty in decision-making
