Development and assessment of an integrated largescale hydrological modelling tool for water resources management in the Cauvery Catchment, India.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.

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.