Engineering topics
Gautam, Sagar
Publications and source records attributed to Gautam, Sagar.
Observational benchmarks inform representation of soil organic carbon dynamics in land surface models
Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.
Impact of bioenergy feedstock carbon farming on sustainable aviation fuel viability in the United States
Biomass-derived sustainable aviation fuel holds significant potential for decarbonizing the aviation sector. Its long-term viability depends on crop choice, longevity of soil organic carbon (SOC) sequestration, and the biomass-to-biojet fuel conversion efficiency. We explored the impact of fuel price and SOC value on viable biojet fuel production scale by integrating an agroecosystem model with a field-to-biojet fuel production process model for 1,4-dimethylcyclooctane (DMCO), a representative high-performance biojet fuel molecule, from Miscanthus, sorghum, and switchgrass. Assigning monetary value to SOC sequestration results in substantially different outcomes than an increased fuel selling price. If SOC accumulation is valued at $185/ton CO 2 , planting Miscanthus for conversion to DMCO would be economically cost-competitive across 66% of croplands across the continental United States (US) by 2050 if conventional jet fuel remains at $0.74/L (in 2020 US dollars). Cutting the SOC sequestration value in half reduces the viable area to 54% of cropland, and eliminating any payment for SOC shrinks the viable area to 16%. If future biojet fuel prices increase to $1.24/L-Jet A-equivalent, 48 to 58% of the total cultivated land in the United States could support a more diverse set of feedstocks including Miscanthus, sorghum, or switchgrass. Among these options, only 8–14% of the area would be suitable exclusively for Miscanthus cultivation. These findings highlight the intersection of natural solutions for carbon removal and the use of deep-rooted feedstocks for biofuels and biomanufacturing. The results underscore the need to establish clear and consistent values for SOC sequestration to enable the future bioeconomy.
Increased drought and extreme events over continental United States under high emissions scenario
The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.
Perspectives on Artificial Intelligence for Predictions in Ecohydrology
Abstract In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.