Dr. SAGAR GAUTAM

Senior Member of Technical Staff · Environmental Engineer @ Sandia National Laboratories

Soil Carbon • Hydrology • Climate Extremes • Bioenergy • ESMs • Machine Learning

RESEARCH PROFILE

Advancing Soil–Climate Intelligence for a Resilient Bioeconomy

I develop integrated modeling approaches that fuse process-based models, Earth System Models, and machine learning to quantify climate impacts on soils, water resources, greenhouse gases, and bioenergy supply chains. My work spans field to global scales, with an emphasis on benchmarking model predictions against observations and informing decision-support tools for sustainable land management.

Based at Sandia National Laboratories (Livermore, CA), I collaborate across national labs and universities to improve representation of soil processes in large-scale models and to evaluate climate extremes, drought, and land-use transitions affecting soil organic carbon.

Soil Biogeochemistry

Carbon & nitrogen cycling, GHG fluxes, microbial processes, and observational benchmarks for model improvement.

Hydrology & Extremes

Downscaling CMIP6, drought metrics, flood/runoff simulation, and ecohydrologic responses under climate change.

ML & Decision Tools

Reduced-order models, ensemble ML for SOC mapping, and LCA-linked analytics for agricultural & bioenergy cropping systems.

10+
First‑Author Papers
$1.6M+
DOE‑Funded Projects
10+
Years in Modeling
4
University Courses (TA/Co‑Instructor)

PROJECT OPERATIONS

Soil Carbon Transitions (PI)

Machine‑learning estimation of land‑use transitions needed across CONUS to buffer climate‑induced soil carbon emissions.

DOE • 2023–2024

Microbial Dynamics in ESMs (Co‑PI)

Representing soil microbial processes in Earth System Models to evaluate impacts of climate extremes on dryland SOC dynamics.

DOE • 2023–present

SOC Loss Under Warming

Benchmarking ML projections against ESMs suggests CONUS may lose substantial SOC by 2100, guiding mitigation pathways.

Research

Ensemble Agroecosystem Modeling

We integrate DAYCENT, DNDC, and EcoSYS to predict soil organic carbon (SOC) and crop yield across CONUS. The multi-model ensemble quantifies uncertainty and benchmarks projections for mitigation and adaptation planning.

DAYCENT DNDC EcoSYS
Research

Soil Moisture Projections (CONUS)

We fuse machine learning with process-based models to map current soil moisture and project future changes across CONUS under CMIP6 scenarios—capturing spatial patterns, trends, and uncertainty for climate-smart planning.

Machine Learning Process Models CMIP6 SSPs CONUS
Research

SELECTED PUBLICATIONS

Permafrost Active Layer (Alaska)

Machine‑learning & process‑based comparison for spatiotemporal ALT variation. Scientific Reports (rev., 2025).

Reduced‑Order Models for SOC & Yield

ML surrogates for bioenergy crops. Carbon Capture Science & Technology (2025).

CONUS Drought & Extremes

High‑emissions scenario analysis of future extremes. Scientific Reports (2023).

SAF Viability

Impacts of carbon farming on sustainable aviation fuel. PNAS (2023).

Full list available on Google Scholar and CV.

TOOLS & MODELS

DAYCENT • DNDC • EcoSYS
SWAT • APEX • MODFLOW • HEC‑RAS
E3SM • CESM (Land Models)
Python • R • MATLAB • HPC • GIS
SQL • ArcGIS • Open‑source Geo
LCA Integration • Decision Support

RESEARCH COLLABORATION

For proposals or collaboration inquiries, please reach me at sgautam@sandia.gov
Senior Member of Technical Staff, Environmental Engineer — Sandia National Laboratories