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

• Grant number: 17/12567-6
• Support type: Scholarships abroad - Research Internship - Doctorate
• Effective date (Start): November 06, 2017
• Effective date (End): August 05, 2018
• Field of knowledge: Biological Sciences - Ecology - Applied Ecology
• Principal researcher: Maria Victoria Ramos Ballester
• Grantee: Rodnei Rizzo
• Supervisor abroad: Martha Anderson
• Home Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Research place: Beltsville Agricultural Research Center, United States
• Associated to the scholarship: 13/20377-1 - Development of soybean virtual water map for the Upper Xingu basin, Mato Grosso - Brazil, BP.DR

Abstract

The southeastern Amazonia has been submitted to a rapid change in land use. There are studies indicating that combined effects of forest loss and climate change have altered the energy and water balances components in many regions of Amazon Basin. Evaluate the changes on water balance components of Amazon region is essential to understand how the anthropogenic activities and the climate change have altered the freshwater resources availability. A limiting factor for hydrological studies in the region is the lack of hydrological information, there is a reduced number of climate stations and river gauges in the Amazon sub-basins. An alternative to conventional ground data are the remotely sensed data. Information from remote sensors has been used to retrieve precipitation, evapotranspiration and water storage change of Amazon Basin, but one of most challenging activities is to define the spatio-temporal dynamics of ET. Many ET models require thermal infrared radiation from cloud free images. In the Amazon Basin, cloud free images are difficult to obtain, particularly during the rainy season. Microwave data is less affected by cloud cover and provides continuous surface information even in cloudy days. It also allows users to retrieve land surface parameters and even derive ET estimates. Therefore, this study will address the following questions: (a) Is it possible to accurately predict ET on a seasonally cloud covered region, combining thermal, microwave and optical remote sensing information? (b) How land use changes have influenced water recycling process in an Amazon sub-basin, the Upper Xingu basin (UX)? (c) Does the TRMM and GRACE products correctly describes spatio-temporal variations of rainfall and water storage over the region? (d) Is it possible to close the water balance of the UX using remotely sensed data? To accomplish this task, it will be created a database containing gridded weather data, spatial information from thermal, optical and microwave sensors and land use maps. We will acquire land surface temperature (LST) data derived from MODIS for clear sky days, and specifically for cloudy days the LST from AMSR-E and TRMM data. This information will be provided to the ALEXI model (Polar-MW-ALEXI), which will describe daily ET in UX, considering the last 15 years. Later, ALEXI estimates will be validated based on flux tower data from the "Large Biosphere-Atmosphere Experiment in Amazonia" (LBA). The TRMM and GRACE estimates will be gathered for the entire study period and later validated based on measurements from rainfall and river gauges, respectively. Once the ET, P and ”s were validated, these data will be used in the water balance closure of the UX basin. The estimated value of Q, from the balance closure, will be compared to in situ measurements from a gauge located in Xingu river. Based on the activities described herein, we expect to: (a) predict ET on the study site, even in cloudy days; (b) indicate a better predictive potential of Polar-MW-ALEXI than the products currently available (e.g. MOD16) (c) identify abrupt changes in the basin's water use, due to land use changes; (d) evaluate the potential of remotely sensed hydrological data to perform the water balance closure of the UX. (AU)