- Title
- Effects of soil data input on catchment streamflow and soil moisture prediction
- Creator
- Binesh, A.; Yeo, I. Y.; Hancock, G.
- Relation
- 23rd International Congress on Modelling and Simulation (MODSIM2019). Proceedings of 23rd International Congress on Modelling and Simulation (MODSIM2019) (Canberra, ACT 01-06 December, 2019) p. 1154-1160
- Publisher Link
- http://dx.doi.org/10.36334/modsim.2019.k24.binesh2
- Publisher
- Modelling and Simulation Society of Australia and New Zealand
- Resource Type
- conference paper
- Date
- 2019
- Description
- This study attempts to investigate the impacts of soil data input on hydrologic model performance in simulating streamflow and soil moisture. Two different soil datasets available in Australia were considered: the Digital Atlas of Australian Soil (AoAS) and Soil and Landscape Grid of Australia (SLGA). We quantified the impacts of these two soil databases on hydrologic simulations using Soil Water Assessment tool (SWAT) model. Two separate calibration schemes were set up with two soil databases while keeping other inputs the same. For both cases, SWAT was calibrated to the daily streamflow at the catchment outlet over 2006-2012 (including wet and dry periods) and validated against the dataset over 2013-2015 (wet period), after 3 years warm up period (2003-2005). The soil moisture estimation from calibrated SWAT was then compared with the two radiometric satellite soil moisture products, the Soil Moisture Active Passive (SMAP)-Enhanced 9 km (L3SMP-E) and Soil Moisture and Ocean Salinity (SMOS) 25 km gridded (SMOS CATDS L3 SM 3-DAY) obtained during 2015. This study was conducted in Merriwa catchment, located in the upper part of the Goulburn River basin in Upper Hunter Region of NSW. The simulation results showed very little difference in streamflow prediction due to two different soil inputs. Both models showed very similar streamflow patterns (with similar NSE value of ~ 0.61 for calibration and ~ 0.45 for validation), but different soil moisture estimates. When catchment average near surface soil moisture estimates were compared with the satellite soil moisture products, SWAT calibrated with SLGA showed improved results (with R2 value of 0.52 and 0.66 against SMAP-9 km and SMOS-25 km and RMSE of ~10 %) than that with AoAS (with R2 value of 0.35 and 0.49 against SMAP-9 km and SMOS-25 km and RMSE of 18-22 %). The large differences in simulated soil moisture indicate importance of improved soil data input to capture soil moisture change patterns and significantly different water and energy partitioning for the catchment.
- Subject
- SWAT; streamflow; soil moisture; soil and landscape grid of Australia; atlas of australian soil
- Identifier
- http://hdl.handle.net/1959.13/1460361
- Identifier
- uon:45944
- Identifier
- ISBN:9780975840092
- Language
- eng
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