https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35131 Wed 15 Mar 2023 12:07:39 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:12754 T, discount rate r, and time T are considered. The results are given in terms of mean cumulative annual loss. The discount rate was found to be a key parameter affecting cumulative damage prediction. Since damage often only occurs once a threshold level of hazard has occurred (such as a flood level exceeding the floor level of a house), then the influence of this threshold value on damage is significant. Damage/impact models utilising probabilistic hazard were used to verify the robustness of results. Results are very sensitive to threshold. Time-dependent predictions of damage can help decisionmakers assess the impact of climate change and the economic viability of climate change adaptation strategies.]]> Wed 11 Apr 2018 13:07:55 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:3302 Wed 11 Apr 2018 11:37:12 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:4831 Wed 11 Apr 2018 10:57:25 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17179 Wed 11 Apr 2018 10:31:07 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33560 Wed 04 Sep 2019 12:18:53 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34065 Wed 04 Sep 2019 12:18:45 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45917 Tue 08 Nov 2022 09:32:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:8909 Sat 24 Mar 2018 08:36:43 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:1457 Sat 24 Mar 2018 08:28:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:1458 Sat 24 Mar 2018 08:28:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:20175 Sat 24 Mar 2018 07:51:44 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:18775 Sat 24 Mar 2018 07:51:10 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:18567 Sat 24 Mar 2018 07:50:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21558 Sat 24 Mar 2018 07:49:54 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:6082 Sat 24 Mar 2018 07:44:45 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:25271 Sat 24 Mar 2018 07:38:19 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:29127 Sat 24 Mar 2018 07:32:32 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:3163 Sat 24 Mar 2018 07:18:14 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:3164 Sat 24 Mar 2018 07:18:13 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:3166 Sat 24 Mar 2018 07:18:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23591 40K concentration of the soil samples determined. Relationships between the field ⁴⁰K and NARM ⁴⁰K were investigated using a digital elevation model, the national soil atlas model and the national geology model. Our results showed that the NARM and field data are correlated and that this correlation extends across changing soil types and geology. A complex relationship with topographical features was also determined which needs further investigation.]]> Sat 24 Mar 2018 07:13:22 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45520 3/cm³, against in-situ field data, and an average ubRMSEs of 0.07 cm³/cm³ when compared to the National Airborne Field Experiment 2005 (NAFE'05) soil moisture retrievals. Both models showed promising results over semi-arid regions in estimating soil moisture at a high spatial resolution, but with their own strengths and weaknesses. The findings here provide useful insights on the robustness of the soil thermal inertia relationship across scales and the effects of the model resolution to the downscaled soil moisture estimates. The approach demonstrated encouraging results over semi-arid regions in estimating soil moisture at a high spatial resolution.]]> Mon 31 Oct 2022 14:09:58 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45518 μSM) have shown promising results over arid and semi-arid landscapes. However, the linearity of these algorithms is affected by factors such as vegetation, soil texture and meteorology in a complex manner. This study tested a (i) Regression Tree (RT), an Artificial Neural Network (ANN), and a Gaussian Process Regression (GPR) model based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia, given the ability of machine learning algorithms to capture complex, non-linear relationships between predictors and responses. Downscaled soil moisture from the RT, ANN and GPR models showed root mean square errors (RMSEs) of 0.03, 0.09 and 0.07 cm³/cm³ compared to airborne retrievals and unbiased RMSEs (ubRMSEs) of 0.07, 0.08 and 0.05 cm³/cm³ compared to in-situ observations, respectively. The study showed encouraging results to integrate machine learning techniques in estimating near-surface soil moisture at a high spatial resolution.]]> Mon 31 Oct 2022 14:02:59 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35054 Fri 14 Jun 2019 14:37:38 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:41785 Fri 12 Aug 2022 12:10:32 AEST ]]>