Collaborative research: Cascade “Ecohydromics” in the Amazonian Headwater System
Collaborative research: Cascade “Ecohydromics” in the Amazonian Headwater System
批准号:
2111028
负责人:
Valeriy Ivanov
金额:
$83.38万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-01-01 至 2024-12-31
中文摘要
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英文摘要
Water movement through landscapes supports plant, animal, and human life, and through evaporation affects cloud processes and large-scale atmospheric circulation. The Amazon Basin cycles more water through streamflow and evaporation than any other contiguous forest in the world, and transpiration by trees – water taken up by roots and released to the atmosphere – is a critical part of this cycle. Understanding how plant roots, stems, and leaves interact with soil water to jointly regulate forest transpiration across landscapes is a critical knowledge gap, especially as climate changes. Forests are likely adapted to distinct soil moisture conditions in different parts of Amazonian landscapes. Specifically, forests on elevated plateaus with deep groundwater use water conservatively in order to tolerate drought, while those in wet valleys with shallow groundwater use water freely but may be poorly prepared for droughts of the future caused by the climate change. To understand landscape hydrology, rainforest compositions, and their susceptibility to global change, an integrated understanding of how water flows are regulated from upstream-to-downstream by plants and soil is required. This understanding is also critical for Earth-system modeling used to project the fate of Amazonian rainforests and quantify their future influence on climate. This project links diverse disciplines – plant physiology, ecology, hydrology – and integrates them into a model of landscape function. This project will also help train the next generation of scientists, both in the U.S. and Brazil, on interdisciplinary approaches in research, and through a summer school on computer modeling of vegetation and hydrologic processes. The project will develop a novel science outreach program connecting K-12 students to real-time Amazon tree data as well as a short class curriculum and a series of videos that teach students how to interpret data, understand the broader scientific context, and build a personal connection with scientists and real-time “talking trees” from the world’s most famous tropical forest.This project characterizes landscape variation in physiological and hydrological processes, and integrate observations with watershed modeling and hypothesis testing. Project activities focus on the spatially intricate mosaic of plateaus and valleys characteristic of central Amazonian headwater catchments. This research hypothesizes that: (H1) strong landscape variation in forest transpiration capacity arises from distinct characteristics of trees residing on plateaus (no root access to groundwater) and valleys (root access to groundwater) zones; (H2) previously unquantified “hybrid” soil hydraulics govern soil water fluxes and transit times connecting plateaus and valleys; and (H3) plateau forests influence the composition and function of valley forests by regulating subsurface water flows from higher to lower landscape areas. Study sites are located in the Brazilian Amazon: “KM34” near Manaus contains an instrumented watershed with more historical data and research on hillslope hydrology than any other watershed in a pristine wet tropical forest, and “KM67” near Santarém sits on a broad plateau with previously deployed deep soil moisture pits, allowing the isolation of processes typical for flat, elevated plateaus. Both sites contain eddy flux towers, canopy access walkways, and a rich history of ecological research and available datasets. A new valley subsite near KM67 will serve as an independent replicate of KM34 observations in valleys. Process-based models of vegetation ecophysiology, subsurface hydrology, and groundwater will be parameterized with observations of leaf physiology, tree morphological traits, soil moisture and physical properties, water table, and streamflow. These models will be integrated employing novel tools in probabilistic learning and uncertainty quantification for proper parameterization and validated with independent observations of tree sapflow, and ecosystem gas exchange and energy balance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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