The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in south-eastern Queensland, Australia, for lead-times of 3–12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia

The Feasibility of Growing Switchgrass in China for Lignocellulosic Ethanol Production

Switchgrass (Panicum virgatum L.) is a perennial plant species native to the United States that is capable of adapting to a wide variety of geographic and climate conditions. There are two ecotypes of switchgrass: lowland varieties which favor areas with higher rainfall and longer growing seasons and upland varieties which favor areas with cooler and drier climate conditions with shorter growing seasons. Switchgrass has the capacity to become a significant bioenergy feedstock for lignocellulosic ethanol conversion. The purpose of this dissertation is to determine which regions in China are suitable for switchgrass production, estimate potential biomass yield, and examine the effects of predicted climate change scenarios at the end of the 21st century on potential yields in China. To accomplish these goals, two ecological niche models (Maxent and GARP) are implemented based on known switchgrass presence data throughout the United States to ascertain which regions in China have suitable habitats for its growth. Multiple linear regression analysis was performed on a comprehensive database of 1,190 switchgrass field trials in 39 separate locations across the United States to build a model that estimates potential switchgrass yields across China. Future climate projections (2070 – 2099) from the Hadley Centre Coupled Model, version 3 (HadCM3) global circulation model (GCM) are employed in the multiple linear regression model to make switchgrass yield estimations for the end of the century. The ecological niche modeling results reveal China has large areas of suitable habitat for switchgrass development. The multiple linear regression analysis demonstrates that China has the potential to produce large quantities of switchgrass, even more so than in the United States; however, analysis of the impact of climate change by the end of the 21st Century indicates that warmer temperatures will result in lower yields on average, a substantial reduction in suitable habitat for lowlands, and an expanded habitat range for upland ecotypes. This dissertation concludes that switchgrass should be considered a viable plant species to serve as a bioenergy feedstock for lignocellulosic ethanol production in China, and the results herein offer guidelines regarding optimal regions in the country for switchgrass production

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models