Development and assessment of an integrated largescale hydrological modelling tool for water resources management in the Cauvery Catchment, India.
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Date
2022
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Abstract
Economic development and population growth in southern India have resulted in rapid changes
to land use, land management and water demand, significantly impacting and degrading water
resources. The significant anthropogenic influences across the catchment have contributed to
changes in hydrological functioning. Focussing on the highly contentious inter-state Cauvery
River Catchment, this study aims to address the key scientific challenges faced within this
catchment.
The study was designed to develop an integrated large-scale hydrological model to
improve water resource assessments in a highly heterogeneous and data-scarce region whilst
considering the primary water resource challenges facing the Cauvery Catchment. The Upper
Cauvery region, located in the Western Ghats, acts as the water tower of the catchment. The
rainfall in the region is monsoonal, the topography is complex, and the rain gauge network is
sparse, resulting in the estimation of rainfall being particularly challenging. The scarce rainfall
data available in the Western Ghats region is hindering the understanding of the regional
weather system, and the accepted rainfall dataset for India, Indian Meteorological Department
rainfall grids, are known to have inaccurate estimations within the Western Ghats. The current
knowledge of the meteorology and hydrology of the Upper Cauvery is limited. Additionally,
the anthropogenic impact on local hydrological processes, such as streamflow, groundwater
recharge and evapotranspiration, is poorly constrained. The current understanding of how these
diverse local changes cumulatively impact water availability at the broader catchment scale is
minimal. Small-scale rural water management and urban heterogeneity may strongly affect
water resource availability across southern India. However, how such fine-scale factors
propagate to the river catchment is largely unclear.
The Global Water Availability Assessment (GWAVA) model was applied initially to
the Upper Cauvery region to determine the suitability and compare model results from other
modelling tools applied in the region. Two new versions of the GWAVA model were then
developed. The first aimed to include small-scale runoff harvesting interventions (SSRHIs) into
the model and quantify their impact on catchment water resources to address a renewed
scientific interest in assessing their effectiveness in improving local water resources and the
effects at a catchment scale. The second aimed to enhance the representation of groundwater
and large operational dams whilst maintaining the model’s applicability to regions with low-data availability. The Indian Meteorological Department (IMD) gridded rainfall was compared
to available gauges and selected remotely sensed datasets within the Upper Cauvery region.
GWAVA will be utilised to assess the applicability of the remotely sensed data for a catchment
rainfall estimation.
GWAVA was determined to be a suitable tool to represent the Cauvery Catchment;
however, the importance of an accurate spatial representation of rainfall for input into
hydrological models and that comprehensive dam functionality is paramount to obtaining good
results in this region was highlighted. Furthermore, the average GWAVA, VIC and SWAT
ensemble provided a better predictive ability in catchments with dams than the individual
models. The average ensemble offset uncertainty in input data and poor dam operation
functionality within individual models.
The inclusion of SSRHIs demonstrated that farm bunds appear to have a negligible
effect on the average annual simulated streamflow. In contrast, tanks and check dams have a
more significant and time-varying impact. The open water surface of the SSRHIs contributed
to an increase in evaporation losses across the sub-catchment. The change in simulated
groundwater storage with the inclusion of SSRHIs was not as significant as sub-catchmentscale
literature, and field studies suggest. Including groundwater processes into GWAVA
improved streamflow simulation in the headwater sub-catchments and the representation of the
baseflow component such that low-flow model skill increased approximately 33-67% in the
Cauvery and 66-100% in the Narmada. The existing dam routine was extended to account for
large, regulated dams with two calibratable parameters. The routine improved streamflow
simulation in sub-catchments downstream of major dams, where the streamflow was largely
reflective of dam releases. The model performance was improved between 15 and 30% in the
Cauvery and 7-30% in the Narmada when the regulated dams were considered. The model
provides a more robust representation of the annual outflow volume from major dams, reducing
the average bias from -17% to -1% in the Cauvery and from 14% to 3% in the Narmada. The
daily dam releases were significantly improved in the Cauvery, approximately 26-164%. The
improvement of the groundwater and dam routines in GWAVA proved successful in improving
the overall model performance, the low-flow model skill and bias, and the inclusions allowed
for improved traceability of simulated water balance components. It was found that the IMD rainfall within the high-altitude regions of the Western Ghats
is underestimated, resulting in the under-simulation of streamflow in the Upper Cauvery.
CHIRPS 0.25- and 0.05- degree, MSWEP and PERSIANN remotely sensed rainfall datasets
were applied within this region. None of the individual rainfall datasets provided a more
accurate representation of the rainfall than the commonly utilised IMD grids. However, using
an ensemble of remotely sensed rainfall datasets, primarily the average ensemble, improved the
accuracy of rainfall estimation in the catchment. The ‘off-the-shelf’ remotely sensed rainfall
products provided a high variation in performance against the in-situ rain gauge data. The IMD
grids provided the most accurate representation of rainfall compared to the individual remotely
sensed rainfall datasets, despite underestimating the rainfall depths at high altitudes. In the case
of the Upper Cauvery, the average ensemble provided a more accurate representation of the
rainfall.
An integrated large-scale hydrological model was developed to improve water resources
assessments in a highly heterogeneous and data-scarce region whilst considering the major
water resource challenges facing the Cauvery Catchment. The effects of runoff harvesting
interventions, accounting for hard-rock aquifer groundwater processes and the impact of major
dams were represented. The inclusion of these features improved the model performance
throughout the Cauvery Catchment.
Description
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.