Zhaohui Lin

The effect of sandstorms and air pollution on cause- specific hospital admissions in Taipei, Taiwan

Objective: Relatively little research exists focusing on the impact of air pollution on hospital admissions in Asia compared to the extensive work conducted in the USA and Europe. The issue is of particular importance because of the frequency, intensity and health effects of Asian sandstorms. This work investigates the relation between cause-specific hospital admissions and sandstorms and air pollution in Taipei, Taiwan’s capital. Methods: Time-series analyses of asthma, pneumonia, ischaemic heart disease and cerebrovascular disease hospital admissions were performed for Taipei. An eight-year time period (1995–2002) was considered for various indicators of sandstorms and the pollutants NO2, CO, ozone, SO2, PM10, and PM2.5. Pollution effects based on single-day lags of 0, 1, 2 and 3 days were explored, along with the average of the same day and previous three days (L03). Results: The risk of ischaemic heart disease admissions was associated with several sandstorm metrics, including indicators of high PM10 levels in the Taipei area, indicators of high PM10 at a monitor designed to measure background pollution, the PM coarse fraction, and the ratio of PM10 to PM2.5. However, the lag structure of effect was not consistent across sandstorm indicators. Hospital admissions for this disease were 16–21% higher on sandstorm days compared to other days. This cause was also associated with transportation-related pollutants, NO2, CO and PM2.5. Asthma admissions rose 4.48% (95% CI 0.71% to 8.38%) per 28 μg/m3 increase in L03 PM10 levels and 7.60% (95% CI 2.87% to 12.54%) per 10 ppb increase in L03 ozone. Cerebrovascular disease admissions were associated with PM10 and CO, both at lag 3 days. SO2 exhibited no relation with admissions. Conclusions: Risk of hospital admissions in Taipei may be increased by air pollution and sandstorms. Additional research is needed to clarify the lag structure and magnitude of such effects.

Analysis and Simulation of Human Activity Impact on Streamflow in the Huaihe River Basin with a Large-Scale Hydrologic Model

Abstract A hydrologic model coupled with a land surface model is applied to simulate the hydrologic processes in the Huaihe River basin, China. Parameters of the land surface model are interpolated from global soil and vegetation datasets. The characteristics of the basin are derived from digital elevation models (DEMs) and a national geological survey atlas using newly developed algorithms. The NCEP–NCAR reanalysis dataset and observed precipitation data are used as meteorological inputs for simulating the hydrologic processes in the basin. The coupled model is first calibrated and validated by using observed streamflow over the period of 1980–87. A long-term continuous simulation is then carried out for 1980–2003, forced with observed rainfall data. Results show that the model behavior is reasonable for flood years, whereas streamflows are sometimes overestimated for dry years since the 1990s when water withdrawal increased substantially because of the growing industrial activities and the development o...

Advances in Decision Support Systems for Flood Disaster Management: Challenges and Opportunities

Natural variations in the global climate are governed by complex interactions among the atmosphere, oceans, and land cover. Modern climate models suggest that these variations will continue, but with larger magnitudes and greater variability due to human influences. This is expected to increase the risk of flood disaster events. To improve flood risk management, a flood decision support system architecture is proposed that capitalizes on the latest advances in remote sensing, geographic information systems, hydrologic models, numerical weather prediction, information technology, and decision theory. Specifically, the dynamic climate prediction system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, is discussed in the context of flood management and planning in the Yangtze River valley, China.

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.

Advances in Disaster Modeling, Simulation and Visualization for Sandstorm Risk Management in North China

Dust storms in North China result in high concentrations of airborne dust particles, which cause detrimental effects on human health as well as social and economic losses and environmental degradation. To investigate the impact of land surface processes on dust storms, we simulate two dust storm events in North China during spring 2002 using two versions of a dust storm prediction system developed by the Institute for Atmospheric Physics (IAP) in Beijing, China. The primary difference between the IAP Sandstorm Prediction System (IAPS 1.0) and more recent version (IAPS 2.0) is the land surface modeling. IAPS 1.0 is based on the Oregon State University (OSU) land surface model, whereas the latest version of the dust storm prediction (IAPS 2.0) uses NOAH land surface schemes for land surface modeling within a meteorological model, MM5. This work investigates whether the improved land surface modeling affects modeling of sandstorms. It is shown that an integrated sandstorm management system can be used to aid the following tasks: ensure sandstorm monitoring and warning; incorporate weather forecasts; ascertain the risk of a sandstorm disaster; integrate multiple technologies (for example, GIS, remote sensing, and information processing technology); track the progress of the storm in real-time; exhibit flexibility, accuracy and reliability (by using multiple sources of data, including in-situ meteorological observations); and monitor PM10 and PM2.5 dust concentrations in airborne dustfalls. The results indicate that with the new land surface scheme, the simulation of soil moisture is greatly improved, leading to a better estimate of the threshold frictional velocity, a key parameter for the estimating surface dust emissions. In this study, we also discuss specific mechanisms by which land surface processes affect dust storm modeling and make recommendations for further improvements to numerical dust storm simulations.

Quantifying the attribution of model bias in simulating summer hot days in China with IAP AGCM 4.1

Abstract Using IAP AGCM simulation results for the period 1961–2005, summer hot days in China were calculated and then compared with observations. Generally, the spatial pattern of hot days is reasonably reproduced, with more hot days found in northern China, the Yangtze and Huaihe River basin, the Chuan-Yu region, and southern Xinjiang. However, the model tends to overestimate the number of hot days in the above-mentioned regions, particularly in the Yangtze and Huaihe River basin where the simulated summer-mean hot days is 13 days more than observed when averaged over the whole region, and the maximum overestimation of hot days can reach 23 days in the region. Analysis of the probability distribution of daily maximum temperature (Tmax) suggests that the warm bias in the model-simulated Tmax contributes largely to the overestimation of hot days in the model. Furthermore, the discrepancy in the simulated variance of the Tmax distribution also plays a non-negligible role in the overestimation of hot days. Indeed, the latter can even account for 22% of the total bias of simulated hot days in August in the Yangtze and Huaihe River basin. The quantification of model bias from the mean value and variability can provide more information for further model improvement.

Impact of Urban Surface Roughness Length Parameterization Scheme on Urban Atmospheric Environment Simulation

In this paper, the impact of urban surface roughness length parameterization scheme on the atmospheric environment simulation over Beijing has been investigated through two sets of numerical experiments using the Weather Research and Forecasting model coupled with the Urban Canopy Model. For the control experiment (CTL), the urban surface parameterization scheme used in UCM is the model default one. For another experiment (EXP), a newly developed urban surface parameterization scheme is adopted, which takes into account the comprehensive effects of urban morphology. The comparison of the two sets of simulation results shows that all the roughness parameters computed from the EXP run are larger than those in the CTL run. The increased roughness parameters in the EXP run result in strengthened drag and blocking effects exerted by buildings, which lead to enhanced friction velocity, weakened wind speed in daytime, and boosted turbulent kinetic energy after sunset. Thermal variables (sensible heat flux and temperature) are much less sensitive to variations. In contrast with the CTL run, the EXP run reasonably simulates the observed nocturnal low-level jet. Besides, the EXP run-simulated land surface-atmosphere momentum and heat exchanges are also in better agreement with the observation.

Sensitivity of the IAP two-level AGCM to surface albedo variations

SummaryTwo numerical experiments were performed for sensitivity study of surface albedo, one was a control run in which the albedo values for snow-free surfaces were prescribed as constant; the other was a sensitivity run in which an albedo with seasonal variation was incorporated into the model show that the simulation of precipitation is sensitive to the surface albedo variations, especially those over Eastern Asia and the Sahara. Changes in surface albedo also have an impact on the monthly mean sea level pressure, especially on the July-mean Western Pacific subtropical high. Surface air temperature decreased over most of the Eastern Asia but increases over most of the Antarctica in July.

Short communication: Improved regional hydrologic modelling by assimilation of streamflow data into a regional hydrologic model

River streamflow is closely related to the surface runoff over a river basin. It is a quantity which can be measured with relatively high accuracy. In regional hydrologic models, surface runoff is usually calculated as the residuum of the surface water balance equation and is one of the most uncertain model outputs, especially for mountainous areas. In this paper, we propose a simple method within the framework of a regional hydrologic modelling system (RHMS), which integrates a hydrologic model and a land surface model, to constrain the estimates of surface runoff through progressive assimilation of observed streamflow data based on the channel flow equation. The proposed method is applied to a sub-basin of the Huaihe River Basin in East China, and numerical experiments are carried out to test the effectiveness of successively (from upstream to downstream) assimilating the streamflow data collected at three hydrological stations to RHMS. It is found that with the assimilation, the timing and magnitudes of the modelled flood peaks are much improved and the correction of surface runoff is substantial. The numerical results demonstrate the usefulness of the new method in improving the RHMS runoff-streamflow simulation as well as providing better initial conditions (e.g. channel water heights) for flood forecasting.

Energy Reduction Effect of the South-to-North Water Diversion Project in China

The North China Plain, with a population of approximately 150 million, is facing severe water scarcity. The over-exploitation of groundwater in the region, with accumulation amounts reaching more than 150 billion m3, causes a series of hydrological and geological problems together with the consumption of a significant amount of energy. Here, we highlight the energy and greenhouse gas-related environmental co-benefits of the South-to-North Water Diversion Project (SNWDP). Moreover, we evaluate the energy-saving effect of SNWDP on groundwater exploitation based on the groundwater-exploitation reduction program implemented by the Chinese government. Our results show that the transferred water will replace about 2.97 billion m3 of exploited groundwater in the water reception area by 2020 and hence reduce energy consumption by 931 million kWh. Further, by 2030, 6.44 billion m3 of groundwater, which accounts for 27% of the current groundwater withdrawal, will save approximately 7% of Beijing’s current thermal power generation output.