The structure and mixing dynamics of shallow tropical reservoirs was investigated using data collected in the Kranji Reservoir in Singapore. Water temperature data spanning a 2 month period in 2007 shows that diurnal cycles of stratification and destratification were formed in various locations in the reservoir. Temperature stratification of 0.5°C to 3.5°C was formed during the daylight hours and reduced nightly when cooling occurred. Substantial horizontal redistribution of heat was also observed between the side arms and the main body of the reservoir. The Kranji's dynamics can be summarized in terms of three physical forcing regimes: a solar radiation—dominated regime, a windy regime, and a cold inflow regime. We delineate the three physical forcing regimes by two potentially useful dimensionless numbers that quantify the relative influences of surface heating, wind stirring, and inflow buoyancy fluxes. For most of the measurement period (88% of the data record), the solar radiation—dominated regime characterized the hydrodynamics. In spite of the dominance of solar radiation in setting local stratification, it is shown that Kranji Reservoir is a three‐dimensional system in which there can be significant variations in temperature in the vertical and along‐reservoir directions, as determined by cold inflow events, differential heating, and reservoir releases. Moreover, the data suggest that the dynamical balance of the Kranji system is sensitive to small forcing events, with the timescales of stratification and mixing as short as a day or less.

Abstract-We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong nonGaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

[1] This study is one of the few attempts to close water and heat budgets in tropical lakes and reservoirs on both daily and monthly time scales. A water budget of Kranji Reservoir is constructed for the year of 2007 using data for water level, reservoir gate operation records, and inflow predicted by a catchment rainfall-runoff model. A heat budget of Kranji Reservoir is also constructed for a field deployment period in 2007 using data for surface radiation fluxes measured by a meteorological station, heat fluxes associated with inflows and outflows, and heat content of the water column measured by thermistors. All the components of the water and heat budgets are accounted for on the basis of a complete data set obtained from field measurements and reliable model predictions, including those that were often neglected in the earlier studies, e.g., advective heat. The water budget of Kranji Reservoir is dominated by the discharge and catchment inflow, which are very sensitive to the variations in precipitation. Analysis of the gate operation records in 2007 shows an appreciable amount of the outflow of Kranji Reservoir was released, especially during storm events. The heat budget reveals that net heat flux of this shallow tropical reservoir is dominated by the net surface radiation fluxes and is also highly responsive to variations in stormflow conditions. It is noted that two critical components in the heat budget are latent heat and inflow advective heat, which equal 83% and 71% of net radiation, respectively.

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches. ACM Reference Format:Wan Du, Zikun Xing, Mo Li, Bingsheng He, Lloyd Hock Chye Chua, and Haiyan Miao. 2015. Sensor placement and measurement of wind for water quality studies in urban reservoirs.

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