Abstract

The aim of this work is to investigate the soil water budget across China by means of hydrological modeling under current and future climate conditions and to evaluate the sensitivity to soil parameters. For this purpose, observed precipitation and temperature data (1981–2010) and climate simulations (2021–2050, 2071–2100) at high resolution (about 14 km) on a large part of China are used as weather forcing. The simulated weather forcing has been bias corrected by means of the distribution derived quantile mapping method to eliminate the effects of systematic biases in current climate modeling on water cycle components. As hydrological models, two 1D models are tested: TERRA-ML and HELP. Concerning soil properties, two datasets, provided respectively by Food and Agriculture Organization and U.S. Department of Agriculture, are separately tested. The combination of two hydrological models, two soil parameter datasets and three weather forcing inputs (observations, raw and bias corrected climate simulations) results in five different simulation chains. The study highlights how the choice of some approaches or soil parameterizations can affect the results both in absolute and in relative terms and how these differences could be highly related to weather forcing in inputs or investigated soil. The analyses point out a decrease in average water content in the shallower part of the soil with different extents according to climate zone, concentration scenario and soil/cover features. Moreover, the projected increase in temperature and then in evapotranspirative demand do not ever result in higher actual evapotranspiration values, due to the concurrent variations in precipitation patterns.

Abstract

Within the European research project SimpleFleet, a fleet management system for small and medium enterprises has been developed. GPS traces are collected from equipped vehicles and fed into an FCD processing system to generate traffic data. To estimate the quality of derived traffic data and determine the systematic bias of a FCD processing system a previous method called “self-evaluation” was applied for specific times of day and roads with similar characteristics. With the assumption that the relative bias is the same for corresponding traffic situations, it can be transferred and used to correct results of further computations. Notice that this opens up a convenient road for automated online corrections based on really large amounts of data, rather than on small data sets from costly measurement campaigns. Document type: Part of book or chapter of book