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Abstract
Abstract Catchment Morphing (CM) is a newly proposed approach to apply fully distributed models for ungauged catchments and has been trialled in several catchments in the UK. As one of the most important input datasets for hydrological models, rainfall spatial variability is influential on the stream variabilities and simulation performance. A homogenous rainfall was utilized in the previous experiments with Catchment Morphing. This study applied a spatially distributed rainfall from CEH-GEAR rainfall dataset in the morphed catchment for ungauged catchments as the follow-on study. Three catchments in the UK were used for rainfall spatial analysis and CEH-GEAR rainfall data were adopted for additional spatial analysis. The results demonstrate the influence of rainfall spatial information to the model performance with CM and illustrate the ability of morphed catchment to deal with spatially varied information. More spatially distributed information is expected to be introduced for a wider application of CM.
Highlights
The availability of various hydrological models with more complex structures is increasing, the application in ungauged catchments is still limited due to the obstacle of transferring knowledge from gauged to ungauged catchments (Sivapalan 2003)
Increases could be found for Austins Bridge and Halsewater when considering adjusting the runoff with Flood Estimation Handbook (FEH) percentage runoff (PR), and all three catchments had improved performance when adjusting the runoff with real PR
We have applied spatially homogenous data from rain guages and distributed rainfall (1 km CEH-GEAR dataset) respectively to the morphed model to explore the potential for a wider application of Catchment Morphing
Summary
The availability of various hydrological models with more complex structures is increasing, the application in ungauged catchments is still limited due to the obstacle of transferring knowledge from gauged to ungauged catchments (Sivapalan 2003). One reason is that some hydrological models (e.g. conceptual models) have specific assumptions and simplifications to keep the model efficient, which makes the models limited to represent the real catchments. These models always have several parameters to be calibrated and the parameters are site specific and not suitable to be applied in ungauged catchments. The heterogeneity of catchment geomorphology, e.g. its terrain, area, shape, land surface condition, soil types, etc., is the root cause of the difficulty in predicting catchment response (Hrachowitz et al 2013)
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