Regionalization of Hydrological Model to Predict Ungauged Basins
Application of Conceptual Model and Introducing RDS Method
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Author analyzed the existing methods in regionalization studies to Predict Ungauged Basins and considering all the aspects a new methodology is developed, which is named as RDS Method. ROPE(R) Data depth (D)-Spatial Proximity (S) together gets this name RDS. Robust Parameter Estimation (ROPE) algorithm ensure all parameter vectors robust with the following criteria: (i) lead to good model performance over the selected time period (ii) lead to a hydrologically reasonable representation of the corresponding process (iii) insensitive (iv) transferable (can be regionalized). Data depth function is...
Author analyzed the existing methods in regionalization studies to Predict Ungauged Basins and considering all the aspects a new methodology is developed, which is named as RDS Method. ROPE(R) Data depth (D)-Spatial Proximity (S) together gets this name RDS. Robust Parameter Estimation (ROPE) algorithm ensure all parameter vectors robust with the following criteria: (i) lead to good model performance over the selected time period (ii) lead to a hydrologically reasonable representation of the corresponding process (iii) insensitive (iv) transferable (can be regionalized). Data depth function is used to find the boundary or the outlier of the catchments to identify donor catchments. It s also used in ROPE algorithm. Application of the Spatial proximity-one of the earliest approach consists of transferring parameters from neighboring catchments to the ungauged catchment, the inspiration being that catchments that are close to each other should have similar behavior since climate andcatchment conditions should vary evenly in space. Blending this three key (ROPE- Data depth- Spatial Proximity) concept together brought a new light in predicting Ungauged Basins.
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