Scale-aware, Improved Hydrological and Biogeochemical Simulations of the Amazon Under a Changing Climate

Earth System Models (ESMs) predict increased frequencies of extremely wet and dry periods in the Amazon over the next century, resulting in very uncertain Amazonian carbon budgets. Because the water cycle strongly influences the carbon cycle and other climate processes and feedbacks, we contend that accurately estimating CO2 and CH4 emissions from upland and floodplains in ESMs requires progress on three fronts: improved hydrologic descriptions, aquatic biogeochemistry, and improved spatial scaling of hydrologic and biogeochemical processes. This project will address these three issues by developing in the Community Land Model (CLM) a multi-scale hydrological and biogeochemical modeling framework based on a subgrid-parameterization scheme and scale-aware downscaling techniques. This approach will bridge the gap between coarse-resolution and fine-scale hydrological and biogeochemical predictions. The goal of the multi-scale framework is to predict CLM gridcell-scale states and fluxes consistent with fine-resolution simulations at orders of magnitude lower computational cost. The hydrologic descriptions will be built upon a highly efficient, physically-based model (Process-based Adaptive Watershed Simulator; PAWS) that has been applied and tested in several temperate watersheds. This framework will greatly reduce uncertainties of hydrologic-biogeochemical simulations due to spatial scaling, enhance our simulation capabilities, and enable uncertainty quantification and multi-objective optimization at climate scales.  We will also integrate a novel aquatic ecosystem model based on previous studies of floodplain carbon dynamics. With these new process representations in CLM we will evaluate the impact on Amazonian carbon budgets of increased frequency of wet and dry periods. We will also develop our scaling approach in a generalizable manner so that our results will be applicable to global simulations over decadal to centennial time scales.

Project Term: 
2013 to 2016
Project Type: 
University Funded Research

Publications:

Accurate and Efficient Prediction of Fine-Resolution Hydrologic and Carbon Dynamic Simulations from Coarse-Resolution Models
Are channels standalone? Analysis of channel-land interactions using PAWS+CLM
Characterizing Coarse-Resolution Watershed Soil Moisture Heterogeneity using Fine-Scale Simulations
Full-Flow-Regime Storage-Streamflow Correlation Patterns Provide Insights into Hydrologic Functioning over the Continental US
Geomorphological significance of at-many-stations hydraulic geometry
High Rates of Methane Oxidation in an Amazon Floodplain Lake
Improving Budyko Curve-based Estimates of Long-Term Water Partitioning using Hydrologic Signatures from GRACE
Influence of Plankton Metabolism and Mixing Depth on CO2 Dynamics in an Amazon Floodplain Lake
Interannual Variation in Hydrologic Budgets in an Amazonian Watershed with a Coupled Subsurface–Land Surface Process Model
Investigating soil moisture spatial scaling using Reduced Order Models and analysis of fractal temporal evolution patterns
Quantifying the Effects of Data Integration Algorithms on the Outcomes of a Subsurface - Land Surface Processes Model
Temporal evolution of soil moisture statistical fractal and controls by soil texture and regional groundwater flow
The Fan of Influence of Streams and Channel Feedbacks to Simulated Land Surface Water and Carbon Dynamics
The Introspective May Achieve More: Enhancing existing geoscientific models with native-language emulated structural reflection
The Introspective May Achieve More: Enhancing existing geoscientific models with native-language emulated structural reflection
The Potential Impact of New Andean Dams on Amazon Fluvial Ecosystems

Research Highlights:

New Study Examines Storage-Streamflow Correlations using Data Mining Approaches to Reveal Fundamental Controls of Hydrologic Responses Highlight Presentation
New Work Proposes Novel Infrastructural Updates that will Accelerate Earth System Modeling Highlight Presentation