|Liu, Benli -|
|Qu, Jianjun -|
Submitted to: Journal of Soil and Water Conservation
Publication Type: Popular Publication
Publication Acceptance Date: June 19, 2013
Publication Date: July 1, 2013
Citation: Liu, B., Qu, J., Wagner, L.E. 2013. Building Chinese wind data for Wind Erosion Prediction System using surrogate US data. Journal of Soil and Water Conservation. 68(4):104A-107A. Interpretive Summary: This publication presents a method to create a Chinese WINDGEN wind database suitable for use with the Wind Erosion Prediction System (WEPS). This method was developed to provide a convenient, publicly accessible wind database in China which bypasses the problem of obtaining expensive and/or currently unobtainable detailed (hourly) wind data. It can be used in WEPS to conduct process-based wind erosion simulations on agricultural croplands, construction sites, etc. with highly temporal resolution (daily and subdaily) wind data. This station matching method used the more extensive US WINDGEN station records as surrogates for Chinese station records at similar climate locations. Testing results show that the new Chinese WINDGEN wind database consisting of 193 stations successfully reflected the wind erosion characteristics across a large portion of China, even though the wind data were not from meteorological records, but from the matched US stations for each location. The resulting Chinese WINDGEN database is expected to be included in later WEPS releases for user convenience.
Technical Abstract: Wind erosion is a global problem, especially in arid and semiarid regions of the world, which leads to land degradation and atmosphere pollution. The process-based Wind Erosion Prediction System (WEPS), developed by the USDA, is capable of simulating the windblown soil loss with changing weather and field conditions and different manmade management scenarios (Hagen 1991; Hagen 2004; Tatarko et al. forthcoming). Erosion in WEPS is driven by stochastically generated hourly wind data by the WINDGEN program, which is more appropriate than using measured data directly, and thus hourly wind data for the entire day are needed to build the statistical database (Donk et al. 2005). The current version of WEPS contains wind data for 2,718 stations within the United States. When running WEPS, wind data from the nearest station, from a station assigned to a polygon region or interpolated from nearby stations, can be used (Wagner forthcoming). Another database, named CLIGEN, which contains other climate information, including daily temperature, precipitation, solar radiation, etc., is also needed for the WEPS simulation. There is a great potential to extend WEPS to other countries and regions, such as China, which has a similar area, latitude location, and climate diversity to the United States. China is threatened by wind erosion on about 1.6 × 106 km2 (6.18 × 105 mi2) area (one-sixth of the total territory) in the north, northeast, and northwest (Hoffmann et al. 2011; Shi et al. 2004). However, wind data with sufficient time resolution are usually limited, especially in arid regions where wind erosion is serious (Lynch and Edwards 1980), which restrict the application of WEPS as well as many other environmental models. For example, many stations in China have wind data that have been recorded only four times a day, and usually only daily data are easily accessible (Donk et al. 2008). Furthermore, the hourly wind data are usually classified at a high-security level and therefore hard to obtain. Thus, it is worth investigating whether we can use wind data from the current WEPS database for locations that are outside the United States but share similar meteorological conditions. The objective of this work was to establish a method to select the statistical wind data for Chinese locations from the existing WEPS wind database. If the chosen data can adequately represent the other locations’ conditions, it would expand WEPS use for evaluating sites for soil conservation, environmental planning, and related Aeolian research outside the United States.