Abstract:
Streamflow variability is important in basin water resources management to analyze and plan
for the present and future hazards and vulnerabilities affecting effective water management.
The unique feature of soil to hold the moisture regulates the precipitation falling on its surface
generating the variability in streamflow. The lack of extensive data for distributed hydrological
models restrains modelers to accurately simulate temporal and spatial variability of streamflow
associated with the soil moisture (SM) in basin-scale. The present study is focused on the use
of a simple hydrologic model to assess the impact of SM on the generation of streamflow
variability in selected dry and wet zone river basins in Sri Lanka and enhance the model
accuracy through the use of satellite soil moisture (SSM) data. The wet and dry zone river
basins, Kalu Ganga and Kirindi Oya basins, respectively with a diverse streamflow variability
were selected for this study. A semi-distributed hydrologic model was developed to model
various events using Hydrologic Engineering Centre’s Hydrologic Modelling System (HECHMS)
with
soil
moisture
accounting
(SMA)
as
the
loss
method.
The
results
obtained
from
the
model
are
compared
with
model
results
forced
with
soil
moisture
active
passive
(SMAP)
SM
data
to
assess
the
impact
of
antecedent
moisture
on
watershed
hydrology.
Events of varying magnitude in terms of discharge and precipitation from both Maha and Yala
seasons were selected considering different return period discharge to calibrate and validate
the model performance. Both models developed for Kirindi Oya and Kalu Ganga performed
well with an average Nash-Sutcliffe Efficiency (NSE) of above 0.73 for calibration and above
0.75 for validation along with average root mean square error (RMSE) and observed standard
deviation ratio (RMSE std dev) below 0.55 for calibration and below 0.48 for validation. The
average coefficient of determination (R
2
) was obtained above 0.80 indicating a strong
correlation. Initial use of SSM improved the model performance of the Kalu Ganga basin
whereas deteriorated the performance of the Kirindi Oya basin. The performance was further
enhanced by optimizing the soil storage and groundwater parameters yielding an average NSE
higher than 0.80, an average R
2
of above 0.90 along with an average RMSE std dev below
0.35 in both basins. Further, the average variation in peak discharge and runoff volume was
reduced to 6 % and 2 %, respectively for Kirindi Oya and 15 % and 10 %, respectively for
Kalu Ganga basins. The overestimated peak discharge and runoff volume were reduced by 28
% and 18%, respectively upon increasing the soil storage parameters whereas the
underestimated peak discharge and runoff volume were increased by 37 % and 43%,
respectively by decreasing the soil storage parameters.
A minor adjustment in soil storage allowed to manipulate and fine-tune the peak discharge and
runoff volume in the basin which substantiates that the runoff is directly associated with the
basin SM. The findings of this study can be useful in basins with similar hydrological
characteristics to understand the role of SM in runoff generation and for sustainable water
management in the basin.
Citation:
Phuyal, U. (2022). Modelling streamflow variability in dry and wet zone river basins in Sri Lanka using satellite soil moisture data [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21920