Flow Regionalization Under Limited Data Availability – Application of IHACRES in the Western Ghats

Abstract

Long-term measurements of river stream flow are essential for numerous applications in water resources. In many parts of the world, rivers remain ungauged; stream flow prediction for such ungauged catchment requires information transfer from gauged catchments that are perceived to be hydrologically similar to them. Prediction of stream flows at ungauged catchments is typically performed through the transfer of hydrologic information (e.g., model parameters, hydrologic indices, stream flow values) from gauged to ungauged catchments. This procedure is commonly referred to as regionalization. Hydrologic models have been used extensively for the purpose of transfer of hydrologic model parameters from gauged to ungauged catchments and these models include HBV, IHACRES and PDM.In the present study, for the purpose of regionalisation, a lumped conceptual model namely, IHACRES has been used. IHACRES is a parsimonious model having six numbers of parameters and requires small data input. The model parameters are used to characterise the daily stream flow of the catchment and are related to the landscape attributes of the catchments. Further, as an offshoot, IHACRES provides for base-flow separation and development of unit hydrograph, which becomes an input in other hydrological works.In the current paper, the model IHACRES is applied at daily time step to six catchments (Malathi, Hemavathi, Lakshmanthirtha, Yetthinahole, Kadumanehalla and Kumaradhara) in the region of Western Ghats, Karnataka. IHACRES, being parsimonious, requires precipitation and temperature as the only data-input. Information is compiled for physical catchment descriptors using the elevation map of the area (DEM) and relationships are developed between the parameters and the descriptors. These relationships are validated by modelling daily stream flow of a gauged catchment (Nethravathi). This work proves that as an alternative model, IHACRES does well in the domain of flow regionalization with least data inputs.