ORCID Profile
0000-0002-3993-1625
Current Organisation
CSIRO Land and Water
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Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2015
Publisher: American Chemical Society (ACS)
Date: 03-08-2010
DOI: 10.1021/ES9039677
Abstract: There is increasing interest in perennial grasses as a renewable source of bioenergy and feedstock for second-generation cellulosic biofuels. The primary objective of this study is to estimate the potential effects on riverine nitrate load of cultivating Miscanthus x giganteus in place of conventional crops. In this study, the Soil and Water Assessment Tool (SWAT) is used to model miscanthus growth and streamwater quality in the Salt Creek watershed in Illinois. SWAT has a built-in crop growth component, but, as miscanthus is relatively new as a potentially commercial crop, data on the SWAT crop growth parameters for the crop are lacking. This leads to the second objective of this study, which is to estimate those parameters to facilitate the modeling of miscanthus in SWAT. Results show a decrease in nitrate load that depends on the percent land use change to miscanthus and the amount of nitrogen fertilizer applied to the miscanthus. Specifically, assuming a nitrogen fertilization rate for miscanthus of 90 kg-N/ha, a 10%, 25%, and 50% land use change to miscanthus will lead to decreases in nitrate load of about 6.4%, 16.5%, and 29.6% at the watershed outlet, respectively. Likewise, nitrate load may be reduced by lowering the fertilizer application rate, but not proportionately. When fertilization drops from 90 to 30 kg-N/ha the difference in nitrate load decrease is less than 1% when 10% of the watershed is miscanthus and less than 6% when 50% of the watershed is miscanthus. It is also found that the nitrate load decrease from converting less than half the watershed to miscanthus from corn and soybean in 1:1 rotation surpasses that from converting the whole watershed to just soybean.
Publisher: American Society of Civil Engineers (ASCE)
Date: 2014
Publisher: IWA Publishing
Date: 30-06-2014
DOI: 10.2166/WP.2014.045
Abstract: As freshwater resources are becoming increasingly scarce, unconventional sources of water should be given new consideration. In coastal cities, seawater, with minimal treatment, can be used for toilet flushing, reducing the demand for freshwater. Currently, it is practised on a large scale only in Hong Kong. This study estimates the cost of seawater flushing and compares it to the cost of wastewater recycling for 15 major coastal cities around the world: Buenos Aires, Chennai, Hong Kong, Jakarta, Karachi, Los Angeles, Miami, Mumbai, New York City, Osaka, San Francisco, Shanghai, Singapore, Sydney and Tokyo. While seawater flushing requires a separate network of mains and, therefore, a greater capital cost, wastewater recycling has a higher ongoing treatment cost. Wastewater recycling, depending on the potability of the recycled water, may also require a separate network of mains, but one with a lower maintenance cost due to its lower vulnerability to corrosion compared to seawater mains. This study finds Chennai, Mumbai and Shanghai to have strong potentials for seawater flushing. That these cities have among the highest population densities in the world and are in the developing world explains their relatively lower unit costs for seawater mains.
Publisher: American Geophysical Union (AGU)
Date: 04-2020
DOI: 10.1029/2019WR025463
Abstract: Earlier researches have proposed algorithms to quantify the measurement uncertainty in rating curves and found that the magnitude of the uncertainty can be significant enough to impact hydrologic modeling. Therefore, they suggested frameworks to include measurement uncertainty in the rating curve to make it robust. Despite their efforts, a robust rating curve is often ignored in traditional practices, considering the investment of time and money as well as the resulting benefit from it. In the current research, we are interested in understanding the role of the measurement error variance in real‐time streamflow forecasting. Our objectives are (i) to employ a state‐of‐the‐art statistical forecasting model that can handle measurement uncertainty in daily streamflow and (ii) to understand the trade‐off in forecasting performance when substantial knowledge regarding the measurement uncertainty is provided to the modeler. We apply the Bayesian dynamic hierarchical model (BDHM) on four gauging sites in the United States. Results show that the BDHM performs better than the daily climatology and local linear regression model. Also, the forecast variance changes proportionally with the change in the error variance as an input in the observation equation. Following this, we design a simulation‐based study, which assigns the measurement error in the reported streamflow to obtain multiple realizations of the true streamflow. The inclusion of substantial knowledge about the true error improves the BDHM's performance by lowering the CRPS (continuous rank probability score) values. However, the inclusion increases the forecast variance to bring the true streamflow within the s ling variability of the forecasted streamflow. Overall, an improved trade‐off between the success rate of forecasts and the forecast variance can be achieved by including the measurement error in the BDHM for rivers that witness less dispersed streamflow data.
Publisher: American Society of Agricultural and Biological Engineers (ASABE)
Date: 2010
DOI: 10.13031/2013.35804
Publisher: American Geophysical Union (AGU)
Date: 09-2011
DOI: 10.1029/2011WR010399
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2004
Publisher: American Geophysical Union (AGU)
Date: 04-2019
DOI: 10.1029/2018WR023857
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2005
Publisher: American Geophysical Union (AGU)
Date: 10-2019
DOI: 10.1029/2018WR024447
Abstract: Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision‐making process of a central manager, an agent‐based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we find that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants however, this policy associates with a high administrative cost. This highlights the trade‐off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward‐based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2023
Publisher: American Geophysical Union (AGU)
Date: 04-2008
DOI: 10.1029/2007WR006126
Publisher: Wiley
Date: 29-06-2011
DOI: 10.1002/BBB.309
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2014
Publisher: American Geophysical Union (AGU)
Date: 11-2017
DOI: 10.1002/2017WR020682
Publisher: American Geophysical Union (AGU)
Date: 08-2016
DOI: 10.1002/2015WR018373
Publisher: Springer Science and Business Media LLC
Date: 27-06-2014
Location: United States of America
No related grants have been discovered for Tze Ling Ng.