This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.
Soil loss due to water erosion, in particular hillslope erosion, can be estimated using predictive models such as the Revised Universal Soil Loss Equation (RUSLE). One of the important and dynamic elements in the RUSLE model is the cover and management factor (C-factor), which represents effects of vegetation canopy and ground cover in reducing soil loss. This study explores the potential for using fractional vegetation cover, rather than traditional green vegetation indices (e.g. NDVI), to estimate C-factor and consequently hillslope erosion hazard across New South Wales (NSW), Australia. Values of the C-factor were estimated from the emerging time-series fractional cover products derived from Moderate Resolution Imaging Spectroradiometer (MODIS). Time-series C-factor and hillslope erosion maps were produced for NSW on monthly and annual bases for a 13-year period from 2000 to 2012 using automated scripts in a geographic information system. The estimated C-factor time-series values were compared with previous study and field measurements in NSW revealing good consistency in both spatial and temporal contexts. Using these time-series maps, the relationship was analysed between ground cover and hillslope erosion and their temporal variation across NSW. Outcomes from this time-series study are being used to assess hillslope erosion hazard, sediment and water quality (particularly after severe bushfires) across NSW at local, catchment and regional scales.
This study aimed to investigate whether lidocaine, alone or in combination with other chemotherapeutic agents, inhibits the growth of human bladder cancer cells in vitro and orthotopically transplanted bladder tumors in vivo. The effects of lidocaine (1.25, 2.5 or 5 mg/mL), mitomycin C (MMC, 0.66 mg/mL), pirarubicin (0.75 mg/mL) and Su Fu’ning lotion (SFN, 0.0625 mg/mL) on the proliferation of human bladder cancer (BIU-87) cells were studied using the MTT assay. A Balb/c nude mouse model of bladder cancer was developed by orthotopic transplantation of BIU-87 cells, and the effects of intravesical instillation of lidocaine and MMC on bladder wet weight (a measure of tumor size) and survival (over 60 days) were studied. Lidocaine inhibited proliferation of BIU-87 cells in a concentration-dependent manner and (when given in combination) enhanced the actions of each of the other antiproliferative agents. In tumor-bearing mice, MMC alone had no effect on mean survival or bladder wet weight. However, the combination of 0.66 mg/mL MMC and 5 mg/mL lidocaine prolonged survival (from 34.62 ± 6.49 to 49.30 ± 6.72 days; n = 8, P < 0.05) and reduced bladder wet weight (from 68.94 ± 53.61 to 20.26 ± 6.07; n = 8, P < 0.05). Intravesical instillation of lidocaine combined with other chemotherapeutic agents potentially could be an effective therapy for bladder cancer.
Considerable seasonal and inter-annual changes exist in rainfall amount and intensity in New South Wales (NSW), Australia. These changes are expected to have significant effect on rainfall erosivity and soil erosion by water, but the magnitude of the impact is not well quantified because of the non-linear and dynamic nature of the relationship between rainfall amount and rainfall erosivity. The primary aim of this study was to model spatial and temporal variations in rainfall erosivity and impacts on hillslope erosion across NSW. We developed a daily rainfall erosivity model for NSW to calculate monthly and annual rainfall erosivity values by using gridded daily rainfall data for a continuous 53-year period including a baseline period (1961-90) and a recent period . Model parameters were improved based on their geographic locations and elevations to be truly geo-referenced and representative of the regional relationships. Monthly and annual hillslope erosion risk for the same periods was estimated with the Revised Universal Soil Loss Equation. We produced finer scale (100-m) maps of rainfall erosivity and hillslope erosion through spatial interpolation techniques, and implemented the calculation of rainfall erosivity and hillslope erosion in a geographic information system by using automated scripts so that it is fast, repeatable and portable. The modelled rainfall erosivity values were compared with pluviograph calculations and previous studies, and the Nash-Sutcliffe coefficient of efficiency is >0.90. Outcomes from this study provide not only baseline information but also continuous estimates of rainfall erosivity and hillslope erosions allowing better monitoring and mitigation of hillslope erosion risk in NSW.
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