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DOE OSTI2023

Historical Pattern Analysis of Global Geothermal Power Capacity Development: Preprint

Between 1913 and 1958, Italy was the only country with an operational geothermal power plant until New Zealand installed its first plant in 1958. At present, 24 countries are involved in the geothermal power market, and they have a combined installed capacity of 16,127 GW. This study analyzes the historical patterns of geothermal power capacity in the world and in individual countries to investigate the ideal global geothermal development pattern by examining the annual cumulative capacity (ACC) and the annual capacity addition (ACA) graphs of the historical development of geothermal power capacity in 24 countries. First, the global patterns are analyzed using these graphs in five periods (1945-1957, 1958-1976, 1977-1991, 1992-2002, and 2003-2020) that are marked by a series of characteristics of ACA peaks separated by two major troughs. Then, five characteristic patterns are developed in five periods globally. These patterns correspond to the early-stage linear, the first acceleration, the first steady-state linear, the second acceleration, and the second steady-state linear developments. A positive relationship exists between global patterns and the 5-year shifted oil-price curve: two major factors influenced global development: 1) increasing oil prices and increasing awareness of global climate change, and 2) global development of geothermal power. Last, we investigate these patterns in each country. The top ten countries, which comprise 93.3% of the world's total installed capacity are separated into five groups based on the availability and characteristics of patterns globally developed in five periods. Group-1 (the United States) has an installed capacity of 3,794 MW, Group-2 (Mexico and Philippines) 963-1935 MW, Group-3 (New Zealand, Italy, Iceland, and Japan) 601-1,037 MW, and Group-4 (Indonesia, Kenya, and Turkiye) 944-2,356 MW. The remaining 14 countries (6.7%), which are called Group 5, are still in an immature stage and have installed capacities of 7-262 MW and are not involved in pattern analysis. Overall, geothermal power in the world is in its third stage of development, which had its peak development after 1977. A fourth development peak may be expected to occur after this through business-as-usual cases. The biggest barrier to the development of the global geothermal power market is the risk associated with exploration and drilling. If risk mitigation systems and funds are employed, the growth of geothermal power production projects could accelerate.

GEOTHERMAL ENERGY↗

DOE OSTI2023

Historical Pattern Analysis of Global Geothermal Power Capacity Development

GEOTHERMAL ENERGY↗

DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗

DOE OSTI2020

Batteries Annual Progress Report (FY2019)

The Vehicle Technologies Office (VTO) of the Department of Energy (DOE) conducts research and development (R&D) on advanced transportation technologies that would reduce the nation’s use of imported oil and would also lead to reductions in harmful emissions. Technologies supported by VTO include electric drive components such as advanced energy storage devices (primarily batteries), power electronics and electric drive motors, advanced structural materials, energy efficient mobility systems, advanced combustion engines, and fuels. VTO is focused on funding early-stage high-reward/high-risk research to improve critical components needed for more fuel efficient (and cleaner-operating) vehicles. One of the major VTO objectives is to enable U.S. innovators to rapidly develop the next generation of technologies that achieve the cost, range, and charging infrastructure necessary for the widespread adoption of plug-in electric vehicles (PEVs). An important prerequisite for the electrification of the nation’s light duty transportation sector is development of more cost-effective, longer lasting, and more abuse-tolerant PEV batteries. One of the ultimate goals of this research, consistent with the current vehicle electrification trend, is an EV which can provide the full driving performance, convenience, and price of an internal combustion engine (ICE) vehicle. To achieve this, VTO has established the following overarching goal (Source: FY2021 Congressional Budget Justification1): …identify new battery chemistry and cell technologies with the potential to reduce the cost of electric vehicle battery packs by more than half, to less than $100/kWh (ultimate goal is $60/kWh battery cell cost), increase range to 300 miles, and decrease charge time to 15 minutes or less by 2028. VTO works with key U.S. automakers through the United States Council for Automotive Research (USCAR) – an umbrella organization for collaborative research consisting of Fiat Chrysler Automobiles (FCA), the Ford Motor Company, and General Motors. Collaboration with automakers through the partnership known as U.S. Driving Research and Innovation for Vehicle Efficiency and Energy Sustainability (U.S. DRIVE) attempts to enhance the relevance and the success potential of its research portfolio. VTO competitively selects projects for funding through funding opportunity announcements (FOAs). Directly-funded work at the national laboratories are awarded competitively through a lab-call process. During the past year, VTO continued R&D in support of PEVs. Stakeholders for VTO R&D include universities, national laboratories, other government agencies and industry (including automakers, battery manufacturers, material suppliers, component developers, private research firms, and small businesses). This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2019 (FY 2019).

25 ENERGY STORAGE↗

DOE OSTI2023

Grid Interconnection and Renewables Deployment Related Air Quality and Human Health Benefits in Southeast Asia

The Association of Southeast Asian Nations (ASEAN) has increasingly focused on multilateral electricity trade, improved grid resiliency and modernization. These opportunities in the power sector have been studied through the ASEAN Interconnection Masterplan Studies, the most recent of which is the ASEAN Interconnection Masterplan Study III (AIMS III). AIMS III assessed four different scenarios of power system in the ASEAN countries that include a base scenario and three additional scenarios (called optimum RE, ASEAN RE, and High RE) considering different levels of deployment of renewable energy (RE) and cross-border generation and trade of electricity for three years (2025, 2030, 2040). In this work, we quantify the potential air quality and public health co-benefits of AIMS III scenarios. For a rapidly expanding, energy hungry region like SE Asia generation is power generation is expected to increase significantly. The AIMS III scenarios' capacity expansion modeling suggest that: 1) Generation nearly doubles from 2025 to 2040 in all four AIMS III scenarios; 2) Most of the increased generation is met by coal in all four AIMS III scenarios; 3) While renewables generation increases in all four AIMS III scenarios, the fraction of total generation (its share) generally decreases because of the much greater increase in non-renewable sources, mostly coal. Only in the High RE Target scenario do renewables represent a higher share in 2040 than in 2025, though even here it is at the expense of natural gas rather than coal; 4) As a result, emissions of gaseous and aerosol pollutants increase significantly in all scenarios. Because emissions increase in 2040 compared to 2025 for all four AIMS III scenarios, so do PM2.5 concentrations. Even for the High RE Target scenario (the scenario with highest share of renewable energy), compared to the Base scenario in 2025, in 2040 PM2.5 concentrations are higher. Compared to the Base scenario in the same years, the Optimum RE and ASEAN RE Target scenarios do not differ very much from the Base in terms of PM2.5 concentration. The High RE Target scenario, on the other hand, yields noticeably lower PM2.5 concentration. For example, in 2040 the population-weighted decrease in annual average PM2.5 concentration in the High RE Target scenario relative to the Base scenario is 0.5 ug m-3. Compared to the Base scenario in 2040, each of the alternative AIMS III scenarios is estimated to result in net reductions in power-sector air quality-related excess mortality in the ASEAN region. Yet there are a few countries for which the Optimum RE and ASEAN RE Target result in increases in PM2.5-related excess mortality (Thailand and Vietnam for the ASEAN RE Target scenario and Thailand for the Optimum RE scenario). However, for the High RE Target scenario, all countries benefit and find reductions in excess mortality resulting from the power sector scenarios modeled in AIMS III with regionwide mortality decreasing by 16,000 compared to the Base scenario in 2040. In summary, our analysis finds that changing power generation emissions is a crucial lever for improving public health in ASEAN member countries and provides a pathway for policymakers to make decision backed by a realistic power sector expansion and air quality analyses.

air quality↗

DOE OSTI2024

Assessing Effects of Climate Change on Legacy Waste at the Enewetak Atoll

The Republic of the Marshall Islands (RMI) is in the central Pacific Ocean ~4,500 km west of Hawaii. The Enewetak Atoll, located in the northwest part of the RMI, was the site for 43 nuclear weapon tests between 1948 and 1958. Fallout and deposition from the tests contaminated the island surfaces, lagoon waters and sediment, and nearby ocean waters at the atoll. In the 1970s, a cleanup effort collected radioactive waste and placed it in the Cactus Crater on Runit Island (also called the Runit Dome). In December 2021, Congress directed the U.S. Department of Energy to study the impacts of climate change on the Runit Dome nuclear waste disposal site. Pacific Northwest National Laboratory (PNNL) assembled a multidisciplinary team of climate scientists, ocean modelers, environmental scientists, and health physicists to assess the likely effects of remaining radionuclides at the Enewetak Atoll. PNNL’s approach focused on effects of tropical cyclones that were postulated to mobilize and transport contaminated lagoon sediments and result in human and biota exposure. PNNL’s study estimated (1) the radionuclide source term, (2) the effects of climate change on severe storms, (3) mobilization and transport of radionuclides, and (4) radiation dose to humans and biota. Radionuclides in the lagoon and/or ocean waters of the Enewetak Atoll were characterized by the U.S. Atomic Energy Commission (AEC) in 1972, Woods Hole Oceanographic Institution in 2015, and Lawrence Livermore National Laboratory in 2018. The RMI Nationwide Radiological Study was conducted in the early 1990s for radionuclides remaining in island soils. The 1972 AEC survey remains the most comprehensive source of radionuclide data on lagoon sediments. Climate change modeling at a regional scale in the central Pacific Ocean is limited. PNNL climate scientists simulated severe historical storms postulated to occur both in a recent climate (2015) and in the future (2090) using the Advanced Research Weather Research and Forecasting (WRF-ARW) model, employing a pseudo-global-warming technique. A postulated complete, future failure of the Runit Dome was also considered. PNNL developed a high-resolution regional ocean hydrodynamics model covering the entire RMI extended economic zone using the Finite Volume Coastal Ocean Model (FVCOM). The FVCOM model was run using global reanalysis data for current climate and WRF-ARW simulation for the future climate. PNNL also developed a radionuclide fate and transport model using the FVCOM Integrated Compartment Model (FVCOM-ICM) to simulate the current and future mobilization and transport of radionuclides sorbed to lagoon sediments and the exchange of radionuclides between the water and sediment. FVCOM-ICM-predicted radionuclide concentrations were then used to estimate radiation dose to humans and biota at all islands of the Enewetak Atoll. Under current climate conditions, annual radiation exposures for the southern islands including Enewetak (Fred) and Medren (Elmer) were below the current U.S. standards. Radiation doses were somewhat elevated starting at Runit Island northward and westward to Enjebi Island (Janet). The islands in the northwest quadrant, particularly Bokoluo (Alice) and Bokombako (Belle), remain relatively contaminated. The islands in the southwestern quadrant have low contamination. The highest contribution to radiation doses comes from consumption of locally grown foods. Two radionuclides, 90Sr and 137Cs, contributed the greatest fraction for most terrestrial foods. In current climate conditions, the storms temporarily increased radionuclide concentrations in the lagoon waters, increasing the radiation dose slightly. In future conditions, doses are expected to be smaller, primarily because of the radioactive decay of the shorter-lived radioisotopes of 90Sr and 137Cs. This could make all islands in the far northwest of the atoll – except Bokombako (Belle) and perhaps Bokoluo (Alice) – suitable for residency. For the f

Prasad, Rajiv↗

DOE OSTI2020

Uranium Mill Tailings Radiation Control Act Title II DOE Due Diligence and Lessons Learned from a Previous Site Transfer - 20352

Title II of the Uranium Mill Tailings Radiation Control Act (UMTRCA) established that a government agency will provide perpetual care for closed uranium and thorium ore-processing sites that were operating under an NRC source material license in 1978 or were licensed thereafter. Commercial owners (licensees) operating under an NRC or agreement state specific license when UMTRCA was passed are responsible for conducting reclamation of any byproduct material remaining from uranium-ore processing operations in accordance with an NRC or agreement state approved reclamation plan. Reclamation includes both surface and groundwater remedies. Upon completion of reclamation and approval by NRC, the site is required to be transferred to either the host state or the DOE for long-term surveillance and maintenance. Since UMTRCA's enactment, six Title II sites have been transferred to DOE; an additional 24 Title II sites are anticipated to be transferred before 2050. DoE's role mandated under UMTRCA Title II as the long-term care custodian is to perform 'monitoring, maintenance, and emergency measures necessary to protect the public health and safety.' UMTRCA requires that the licensee pay a long-term surveillance charge 'sufficient to cover the annual costs of site surveillance.' However, at some sites such as the Bluewater, New Mexico, Disposal Site, this mandate has required additional effort and expense by DOE, beyond the originally anticipated and intended scope within UMTRCA, but within the authority of DOE under UMTRCA. In 1997, the Bluewater site became the second UMTRCA Title II site to be transferred to DOE. The site was the location of a uranium mill operated from 1953 until 1982. The specific licensee began site reclamation in 1991, and by 1995 all tailings and contaminated materials were encapsulated in two tailings disposal cells and other disposal areas. In addition to surface contamination, milling activities impacted groundwater in the two upper aquifers. In 1989, the specific licensee attempted active groundwater remediation; however, no significant reduction in contaminant concentrations was observed. As a result, the specific licensee applied to NRC for alternate concentration limits (ACLs) in 1990, which were approved in 1996 as being protective, after additional corrective actions were performed. Since transfer of the Bluewater site to DOE, unforeseen challenges have occurred, requiring additional actions. The first challenge is the occurrence of surface depressions located on the northern section of the main tailings disposal cell. The depressions were first observed during DoE's initial inspection in 1998; however, evidence of these can be observed on satellite images taken prior to transfer. Since being first observed, the depressions have continued to grow both in depth and areal extent. Due to the design of the main tailings disposal cell, the depressions impede storm water from being effectively shed off the 101- hectare (250-acre) top slope of the main tailings disposal cell. Instead, storm water accumulates in the depressions, forming a large ephemeral pond that has stored up to 16.3 x 10{sup 6} liters (4.3 million gallons) of stormwater. The ponding poses a potential risk to the integrity of the main tailings disposal cell in the case of a large storm event, with the potential to cause the pond to overtop and erode the cover material and underlying waste. DOE has taken a number of short-term actions to monitor, measure, and reduce the ponding and is currently working with the US Army Corps of Engineers to design and construct a repair. Additional challenges are associated with groundwater at the Bluewater site. Nine wells were present on the 1335-hectare (3300-acre) site upon transfer. Groundwater compliance was called into question after the State of New Mexico reduced its uranium groundwater standard from 5.0 to 0.03 milligrams per liter in 2004, and when an ACL for uranium was exceeded in a site monitoring well in 2010. Acquiring historical groundwater data and subsequent evaluations as well as additional DOE groundwater monitoring led to installing 10 new monitoring wells and performing additional site hydrogeology recharacterization. DOE continues to evaluate groundwater conditions at the site and works with the NRC to determine regulatory requirements and a path forward. As a result of lessons learned at the Bluewater site and other Title II sites, improved processes have been implemented at a programmatic level to increase due diligence before site transfer and prevent similar issues from occurring at other UMTRCA Title II sites under long-term management. DoE's due diligence process is documented in the Process for Transition of UMTRCA Title II Disposal Sites to DOE for Long-Term Surveillance and Maintenance and is designed to ensure that DOE has no technical or compliance concerns with regulatory decisions that might compromise protectiveness following site transfer to DOE. Implementation of the due diligence process has increased DoE's role prior to site transfer and has been effective in identifying potential issues. Actions by NRC and specific licensees, in response to enhanced due diligence efforts by DOE, are expected to minimize, if not totally prevent, the need for unanticipated actions by DOE pertaining to the surface and groundwater remedies after site transfer. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗