Xingu: integrating land use planning and water governance in Amazonia: towards imp...
| Grant number: | 13/20377-1 |
| Support type: | Scholarships in Brazil - Doctorate |
| Effective date (Start): | December 01, 2014 |
| Effective date (End): | October 31, 2018 |
| Field of knowledge: | Biological Sciences - Ecology |
| Cooperation agreement: | Coordination of Improvement of Higher Education Personnel (CAPES) |
| Principal Investigator: | Maria Victoria Ramos Ballester |
| Grantee: | Rodnei Rizzo |
| Home Institution: | Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil |
| Associated research grant: | 13/50180-5 - Xingu: integrating land use planning and water governance in Amazonia: towards improved freshwater security, AP.PFPMCG.TEM |
| Associated scholarship(s): | 17/12567-6 - Evaluation of the water balance in southeastern Amazon over the last two decades: employing a remote sensing based methodology to describe the regional water resources, BE.EP.DR |
Abstract The Population growth, increasing agricultural production and the current climate change scenarios indicate possible depletion of water resources. Aiming to develop a mechanism to help in water resources management, the concept of virtual water was created. The Virtual water is usually applied on a global or national scale. However, the optimization in water resources management requires projects on watersheds scales. Therefore, it is essential to implement the methodology in smaller scales. Our study area corresponds to the Upper Xingu basin in Mato Grosso state, which is an important soybean producer. The local soybean production is rainfed and the region is an important supplier to international market, ensuring food and water security in countries like China. This project aims to use remote sensing and other techniques such geostatistics, to optimize the calculation of soybean virtual water on regional scale. In this case, the crop water requirement will be estimated based on SEBAL model and MODIS satellite images. Furthermore, a regression trees algorithm will be calibrated to estimate soybean yield based on precipitation, temperature, solar radiation and MODIS satellite images. This algorithm will be employed to create a map of virtual water. This map will indicate sites resiliency and vulnerability to climate changes, as well as water efficiency of farms in this region. (AU) | |