Award Abstract # 2151651
EAGER: CAS-Climate: AI-driven Probabilistic Technique, Quantile Regression based Artificial Neural Network Model, for Bias Correction and Downscaling of CMIP6 Projections

NSF Org: CBET
Division of Chemical, Bioengineering, Environmental, and Transport Systems
Recipient: NORTH CAROLINA STATE UNIVERSITY
Initial Amendment Date: December 6, 2021
Latest Amendment Date: July 1, 2024
Award Number: 2151651
Award Instrument: Standard Grant
Program Manager: Lucy Camacho
lcamacho@nsf.gov
 (703)292-4539
CBET
 Division of Chemical, Bioengineering, Environmental, and Transport Systems
ENG
 Directorate for Engineering
Start Date: December 15, 2021
End Date: November 30, 2025 (Estimated)
Total Intended Award Amount: $299,543.00
Total Awarded Amount to Date: $353,927.00
Funds Obligated to Date: FY 2022 = $299,543.00
FY 2024 = $54,384.00
History of Investigator:
  • Sankarasubraman Arumugam (Principal Investigator)
    sankar_arumugam@ncsu.edu
  • Brian Reich (Co-Principal Investigator)
Recipient Sponsored Research Office: North Carolina State University
2601 WOLF VILLAGE WAY
RALEIGH
NC  US  27695-0001
(919)515-2444
Sponsor Congressional District: 02
Primary Place of Performance: North Carolina State University
NC  US  27695-7908
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): U3NVH931QJJ3
Parent UEI: U3NVH931QJJ3
NSF Program(s): GOALI-Grnt Opp Acad Lia wIndus,
EnvS-Environmtl Sustainability
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 019Z, 090Z, 1504, 7916
Program Element Code(s): 150400, 764300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Global Climate Models (GCMs) are typically used to develop climate projections to predict extreme events (e.g., droughts, floods). Spatial resolution of GCM projections has improved due to increasing computational power, but is still inadequate for watershed-scale applications where extreme event prediction is needed to enable planning. The research undertaken in this project will develop an AI-based technique to improve hydroclimatic projections at the watershed scale. AI techniques are quite powerful in modeling global climate data and could develop finer spatial and temporal future climatic projections. The potential impact is improved planning for, and resilience to, extreme events at the watershed scale.

This research will develop an AI-based probabilistic approach that uses a Quantile Regression based Artificial Neural Network (ANN) (QR-AI) model for bias-corrected and statistically downscaled (BCSD) Coupled Model Intercomparison Projects (CMIP6) projections. Specifically, the research will develop three BCSD data products of CMIP6 projections over the continental U.S. (CONUS): 1) Historical simulations (1950-2014) of precipitation and temperature of GCMs; 2) Near-term (30 year) hindcasts of precipitation and temperature from relevant GCMs and 3) Near-term (30 year) projections of precipitation and temperature for four different Shared Socioeconomic Pathways, which are represented by CO2 emission and mitigation scenarios. Developing BCSD of both hindcasts and historical projections will provide an opportunity to validate the QR-AI methodology by comparing the uncertainty in the estimated climate variables with the observed marginal density of precipitation and temperature over the CONUS. The BCSD CMIP6 products on precipitation and temperature will be developed using the AI method for the entire CONUS and disseminated through the project website. BCSD data will also be archived in figshare and github for dissemination. Additionally, the investigators will work with focused user groups, such as reservoir management and social media, for active dissemination of the developed BCSD products.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Fang, Shiqi and Johnson, J Michael and Sankarasubramanian, A "Leveraging synthetic aperture radar (SAR) with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins" Environmental Research Letters , v.19 , 2024 https://doi.org/10.1088/1748-9326/ad8808 Citation Details
Majumder, Reetam and Reich, Brian J. and Shaby, Benjamin A. "Modeling extremal streamflow using deep learning approximations and a flexible spatial process" The Annals of Applied Statistics , v.18 , 2024 https://doi.org/10.1214/23-AOAS1847 Citation Details

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