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A geosensor network to adress hydroclimatic environmental services


The proposal seeks to make progress via two pilot studies, with the deployment of wireless hydroclimatic geosensor networks (air temperature and humidity, soil moisture and watertable), over two experimental sites, namely an Atlantic tropical forest watershed, and a Farmland watershed at the PCJ basin which supplies water to Sao Paulo, and targeted measurements to be incorporated into mathematical models of Environmental Services to describe scenarios of multiple and optimized land use in a range of climate conditions. We plan to build on expertise for efficient and low cost instrumentation, with the assembling of commercial sensors with novel and especially designed interfaces and sensors developed and validated at the USP laboratories. The entire system should provide adequate configuration of management software to optimize field installation, data acquisition, storage and maintenance. The architecture should be able to provide the acquisition of large data streams and in an appropriate way for manipulation, with the aim to increase the ability to gather data at spatial and temporal scales to understand hydroclimatic phenomena. We will use the InVEST and the SWAT model. With the field and modeling data, we expect to increase knowledge on how ecosystems respond to changes in natural climate and hydrology across temporal and spatial variability; how human induced forcing such as land use change, for a range of climate change scenarios, affect the water balance in small and meso-scale watersheds in the biomes of the Atlantic Forest; where and how the policies of conservation and adaptation to climate change could be tackled in critical areas; translate scientific advancements into effective tools for conservation, water management and environmental public policies. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MARTIN, THOMAS C. M.; ROCHA, HUMBERTO R.; PEREZ, GABRIEL M. P.. Fine scale surface climate in complex terrain using machine learning. INTERNATIONAL JOURNAL OF CLIMATOLOGY, v. 41, n. 1, . (12/51872-5, 15/50682-6, 12/50343-9)
PADGURSCHI, MAIRA C. G.; VIEIRA, SIMONE A.; STEFANI, EDSON J. F.; NARDOTO, GABRIELA B.; JOLY, CARLOS A.. Nitrogen input by bamboos in neotropical forest: a new perspective. PeerJ, v. 6, . (03/12595-7, 12/51872-5, 07/57465-4, 08/50285-3, 15/50682-6, 12/50343-9)

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