Keywords
rainfall-runoff models, rrmt, semi-distributed, parsimonious modelling, calibration, regionalisation
Start Date
1-7-2008 12:00 AM
Abstract
In operational hydrology identification of an appropriate model structure and suitable parameter sets for a specific catchment is a challenging task. This identification process is often based on data availability, catchment characteristics and modelling objectives, and will often result in a range of different model structures. This process of model identification becomes even more challenging when moving from lumped to distributed models as the potential number of model parameters increases proportionally to the number of spatial units considered, and due to the existence of ungauged spatial units. A Semi-Distributed Rainfall-Runoff Modelling Toolbox (RRMT-SD) has been developed to estimate continuous streamflow at points along the river system using conceptual and hybrid representations of the rainfall-runoff processes which vary from low to medium model complexity. The user can easily implement different model structures and calibration strategies, considering multiple objective functions and Monte Carlo analysis. To show the potential of the toolbox a case study on the Upper Lee catchment (1040 km2), UK, using hourly time-steps is presented. The study area was divided into gauged subcatchments and each one of them represented through smaller spatial units of similar areas. Different model structures were applied on the spatial units using estimated a priori parameter values based on a simple regression method. The models were calibrated using spatial multipliers to adjust the a priori parameter values to the scale of the spatial units. Results showed that for different types of subcatchments (low and high base flow types) two soil moisture model structures (the Probability Distributed Moisture Model and the Catchment Wetness Index, respectively) were justified, and that parsimonious semi-distributed rainfall-runoff models on the Upper Lee catchment can perform reasonably well for a single criteria (e.g. average NSE values of 0.74).
A Toolbox for the Identification of Parsimonious Semi-Distributed Rainfall-Runoff Models: Application to the Upper Lee Catchment
In operational hydrology identification of an appropriate model structure and suitable parameter sets for a specific catchment is a challenging task. This identification process is often based on data availability, catchment characteristics and modelling objectives, and will often result in a range of different model structures. This process of model identification becomes even more challenging when moving from lumped to distributed models as the potential number of model parameters increases proportionally to the number of spatial units considered, and due to the existence of ungauged spatial units. A Semi-Distributed Rainfall-Runoff Modelling Toolbox (RRMT-SD) has been developed to estimate continuous streamflow at points along the river system using conceptual and hybrid representations of the rainfall-runoff processes which vary from low to medium model complexity. The user can easily implement different model structures and calibration strategies, considering multiple objective functions and Monte Carlo analysis. To show the potential of the toolbox a case study on the Upper Lee catchment (1040 km2), UK, using hourly time-steps is presented. The study area was divided into gauged subcatchments and each one of them represented through smaller spatial units of similar areas. Different model structures were applied on the spatial units using estimated a priori parameter values based on a simple regression method. The models were calibrated using spatial multipliers to adjust the a priori parameter values to the scale of the spatial units. Results showed that for different types of subcatchments (low and high base flow types) two soil moisture model structures (the Probability Distributed Moisture Model and the Catchment Wetness Index, respectively) were justified, and that parsimonious semi-distributed rainfall-runoff models on the Upper Lee catchment can perform reasonably well for a single criteria (e.g. average NSE values of 0.74).
