Name
Title | ||
|---|---|---|
Assessment of the internal dynamics of the Australian Water Balance Model under different calibration regimes. |
Abstract | ||
|---|---|---|
Conceptual rainfall runoff models are used extensively in practice, as they provide a good balance between transparency and computational and data requirements. However, the degree to which they are able to represent underlying physical processes is poorly understood. This is because the performance of such models is generally assessed based on their ability to match total streamflow, rather than component processes. In this paper, the ability of the Australian Water Balance Model (AWBM) to represent baseflow and quickflow is assessed for 66 synthetic catchments with different physical characteristics and hydrological inputs under seven calibration regimes utilising a shuffled complex evolution (SCE) algorithm. The \"observed\" total-, base- and quick-flow hydrographs for these catchments are generated using HydroGeoSphere. The results indicate that while AWBM is generally able to match total streamflow well, the same does not apply to baseflow and quickflow, suggesting that these processes are not represented well by AWBM. Introduce method to assess the internal dynamics of AWBM under calibration regimes.Method applied to 66 synthetic catchments with different conditions.Traditional calibration predicts streamflow well, not baseflow (BF) & quickflow (QF).Consideration of internal dynamics in calibration improves BF & QF prediction.Results from the paper show AWBM structure could possibly be improved. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.envsoft.2014.12.015 | Environmental Modelling and Software |
Keywords | Field | DocType |
calibration,regression models,baseflow | Streamflow,Water balance,Regression analysis,Hydrology,Computer science,Surface runoff,Baseflow,Hydrograph,Calibration,Precipitation | Journal |
Volume | Issue | ISSN |
66 | C | 1364-8152 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Li | 1 | 0 | 0.34 |
| M. F. Lambert | 2 | 14 | 2.16 |
| Holger R. Maier | 3 | 738 | 72.97 |
| Daniel Partington | 4 | 2 | 0.72 |
| Craig T. Simmons | 5 | 0 | 0.34 |