Narrow Results

Even with a normalized and standardized biofuel shock, the wide range of land-use change estimates and their associated greenhouse gas (GHG) emissions have raised concern on the adequacy of existing agricultural models in this new area of analysis. In particular, reducing bias and improving precision of impact estimates are of primary concern to policy makers. This paper provides a detailed overview of the FAPRI-CARD agricultural modeling system, with particular emphasis on the modifications recently introduced to reduce bias in the results. We illustrate the impact of these new model features using the example of the new yield specification that now includes updated trend parameter, intensification and extensification effects, and a spatially disaggregated Brazil specification. The paper also provides a taxonomy of the many types of uncertainty surrounding any analysis, including parameter-coefficient uncertainty and exogenous variable uncertainty, identifying where specific types of uncertainty originate, and how they interact. Finally, FAPRI-CARD's long experience in using stochastic analysis is presented as a viable approach in addressing uncertainty in the analysis of changes in the agricultural sector, associated land-use change, and impacts on GHG emissions.

Cities are one of the major contributors to climate change and suffer potentially high health and environmental risks and great damages by natural disasters under a changing climate. Extreme rainfall events, often causing flash floods in urban areas, are one of the costliest natural disasters. In particular, rapid urbanization under local climate change often exerts strong impacts on extreme precipitation. Thus, understanding the characteristic changes in various aspects of extreme precipitation due to the urbanization processes is of utmost importance to properly plan and manage potential urban disasters. In numerical models, an urban region is usually represented by a land cover with low reflectivity, low heat capacity and water availability, and a large roughness length, whose characteristics can change according to the degree of urbanization. This study aims to quantify the effect of urbanization on various aspects of extreme precipitation, including its formation, development, location and amount, by conducting sensitivity experiments in terms of urban land cover/use for an extreme rainfall case in the Seoul metropolitan area, Korea, using the Weather Research and Forecast (WRF) model coupled with Urban Canopy Model (UCM) --- the WRF-UCM. Our results provide a basis for better quantitative precipitation forecasting and a reference for urban planning and design.