| dc.description.abstract | As the global population grows, the demand for safe water access increases. At the same
time, increased wastewater disposal from urban activities and reduced river dilution capacity
contribute to the deterioration of water quality, further leading to water scarcity. However,
challenges associated with consecutive and integrated water quality monitoring create a
significant barrier to effective water quality management.
To address these challenges, the dynamical surface water quality model (DynQual) has been
developed to simulate water temperature, as well as the concentration of Total Dissolved
Solids (TDS), Biological Oxygen Demand (BOD), and Fecal Coliform (FC). This water quality
model operates at a daily time step with a spatial resolution of 5 x 5 arcmin, equivalent to 10
km at the equator.
This study ran the DynQual model at 1 km resolution to evaluate its feasibility and potential at
finer scales. The evaluation was done by comparing the performance of the 1 km resolution
model to models with different resolutions. A downscaling and upscaling technique was
applied to match the designated resolution. The outputs of each model were validated to the
local observations data by using error statistics. The model performance was assessed by
using bias and correlation, in which the most accurate model is the one with the smallest bias
and strongest correlation.
The error statistics indicate that the correlation differs across resolution and parameters, while
the 1 km resolution model exhibits the most significant bias, particularly in simulating BOD,
FC, and TDS. However, this study also suggests that the model is able to capture across
different seasons. Here, the study explores the challenges encountered when running the
DynQual model at different resolutions. Further adjustment and calibration in parameterization
may be required to improve the model’s accuracy at finer resolution. | |
