Spatial And Temporal Analysis Of Net Primary Productivity And Water Use Efficiency In Yellow River Source Region

With more and more human activities in West of China, the pressure on environment and ecology is getting greater. Shortage of water or soil-water loss causes poverty. Water Use Efficiency (WUE) is the key factor linking the carbon nitrogen cycle and water cycle in the vegetation ecology system; therefore it has special ecological and hydrological meaning. To use the limited water resource in West of China reasonably and effectively, we should comprehensively and deeply understand the characteristics of WUE in different vegetation ecological systems, find out and popularize the vegetations which are of low-water-consumption but high -production - capability, improve the use efficiency of irrigation water in agricultural ecosystem and prepare for the sustainable use of water resource.In this thesis, based on CASA model (Carnegie Ames Stanford Approach) in ENVI, with MODIS products, meteorological data, vegetation and soil data, NPP (Net Primary Productivity) in yellow river region from 2001 to 2005 is obtained. The spatial distribution and temporal change characters of NPP are analyzed. According to NPP and potential evapotranspiration, WUE in yellow river region from 2001 to 2005 is obtained and also its spatial distribution and temporal change characters are analyzed. The NPP and WUE between different kinds of vegetation are analyzed; the relationship between influence factors and NPP and WUE is analyzed. The research conclusions are as follows:1. Based on CASA model, with MODIS high temporal resolution data products, meteorological data, vegetation and soil data, NPP in Yellow River source region is calculated. In this way, the shortcoming in the traditional statistic model can be avoided, the precise of NPP can be improved and the temporal and spatial variation of NPP can be reflected better; the applicability of CASA model in yellow river source region is validated..2. As to the spatial distribution of NPP, most of the quantity is in the mid lower part of the source region Quantities around Tongde station and Henan station are relatively concentrative and Dari station and its surrounding followed; In contrast, NPP distributed in Guoluo station, Jiuzhi station and Dari station is scattered, the part above Dari station and Guoluo station has rather low quantity, the lowest region is lakes and the bare mountains with a high elevation.3. As to the temporal change of NPP, different regions have different responses. For the whole region, NPP in 2003 reaches maximum; As to the month change, NPP grows rapidly in March, reaches the maximum in July, and reduce rapidly in September.4. As to different kinds of vegetations, they have different tendencies in NPP change between 2001 and 2005. Some vegetations are in the increased tendency all through, some grow between 2001 and 2003 and reduce between 2003 and 2005, others are always fluctuant and several reaches maximumin 2004. 5. As to the temporal change of WUE, it increases between 2001 and 2005 as a whole, but that in 2004 decreased a little. Referring to the mensal mean value in every year, WUE reaches the maximum in June, decreases much in July, and rallies in August and September, and then decreases rapidly.6. As to different kinds of vegetations' WUE, it has the similar tendency with that of annual change of NPP. Three kinds of swamp are high in WUE. In other kinds, the one of temperate deciduous broad-leaf forest is the highest; others are in turn as follows, cropland vegetation, temperate grassland, salinization meadow, alpine meadow, and so on. Lakes and bare regions have the lowest WUE.7. After analyzing the relationship between NPP, WUE and their influence factors, such as, temperature, precipitation, solar radiation, NDVI, it is easy to find out that NPP is close related to NDVI, and then temperature and elevation, precipitation and solar radiation are poorly related. Vegetation growth in the source region, especially in dry season, mainly depends on unfreezed water from frozen soil, glacier and snow. WUE is a coupling value of NPP and evapotranspiration, which is close related to NDVI, solar radiation followed, which mainly affects evapotranspiration; precipitation and temperature are poorly related to WUE and the worst one is elevation.