Abstract:
Precipitation varies significantly over space and time within a watershed. Precipitation has a
vital role in determining surface hydrological processes because of its influence on streamflow
estimations using mathematical models. Though monthly rainfall data provides ease of access
due to availability and affordability, daily data is the preferred option of engineers, planners
and water managers. This is because daily time resolution is considered as a unit which
reasonably represent the catchment time lag. If a water model calibrated using monthly data
could estimate daily streamflow from a watershed, then this would be of immense value for
sustainable water resources management. The three-parameter monthly water balance model
(3PMWBM) proposed by (Dissanayake, 2017) has demonstrated the capability with an
application on 2 watersheds in Sri Lanka while using Thiessen averaging method for rainfall
input. Wijesekera and Musiake (1990a, 1990b) had optimized both rainfall station weights
and model parameters for improved streamflow estimations by enabling the calibration of
point rainfall measurements to generate a spatially averaged rainfall to reflect the response of
the corresponding watershed. The study objective is to estimate streamflow in daily timescale
using a monthly water balance model while optimizing the spatial variability of rainfall
leading to enhanced water security and sustainable water management. Daily data from 2005
to 2014 of 4 rainfall stations of Badalgama watershed (1360 km2) in Ma Oya Basin, Sri Lanka
are used to evaluate the streamflow predictions with the 3PMWBM when rainfall station
weights are optimized. The 3PMWBM was developed, calibrated and verified with and
without optimizing the rainfall gauging station weights. A spreadsheet tool and an object
oriented modelling tool was used for the model development. Mean Ratio of Absolute Error
(MRAE) was selected as the objective function during calibration and verification. The high,
medium and low flow determined from observations and annual water balance were also were
used during evaluation. The optimum value based on literature and analysis for Sc, C and k
are 908, 2.5 and 0.69 respectively for monthly model. The MRAE calibration and verification
results obtained at consecutive steps 0.41,0.409 and 0.36 and 0.60,0.62,0.50 i.e. optimizing
model parameters, optimizing rainfall weights, optimizing model parameter and rainfall
weights at the same time Thiessen weights are (0.26,0.19,0.20,0.35), (0.20,0.16,0.26,0.38) and
(0.23,0.14,0.27,0.36) respectively for Ambepussa, Andigama, Aranayake and Eraminigolla
stations. Daily streamflow estimations in Badalgama watershed using 3PMWBM with the
optimization of rainfall station weights with optimum average MRAE 0.64. The study found
that spatial variability of rainfall can significantly affect model results about 17%
improvement in average MRAE at monthly scale when station weights and parameters are
simultaneously optimized and under same case when the model is used for daily streamflow
estimation, up to 8% improvements in average MRAE are noticed.
