DOCSLIB.ORG

Evaluating Soil Biogeochemistry Parameterizations in Earth System Models with Observations

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evaluating Soil Biogeochemistry Parameterizations in Earth System Models with Observations PUBLICATIONS Global Biogeochemical Cycles RESEARCH ARTICLE Evaluating soil biogeochemistry parameterizations 10.1002/2013GB004665 in Earth system models with observations Key Points: William R. Wieder1, Jennifer Boehnert1, and Gordon B. Bonan1 • SOC estimates from ESMs show wide variation and are exceptionally low 1National Center for Atmospheric Research, Boulder, Colorado, USA in CLM4cn • After modifications DAYCENT para- meterizations provide more realistic SOC pools Abstract Soils contain large reservoirs of terrestrial carbon (C), yet soil C dynamics simulated in Earth • SOC responses to warming suggest systems models show little agreement with each other or with observational data sets. This uncertainty further evaluation of models underscores the need to develop a framework to more thoroughly evaluate model parameterizations, are warranted structures, and projections. Toward this end we used an analytical solution to calculate approximate equilibrium soil C pools for the Community Land Model version 4 (CLM4cn) and Daily Century (DAYCENT) soil Supporting Information: biogeochemistry models. Neither model generated sufficient soil C pools when forced with litterfall inputs • Readme • Text S1 from CLM4cn; however, global totals and spatial correlations of soil C pools for both models improved when • Text S2 calculated with litterfall inputs derived from observational data. DAYCENT required additional modifications • Text S3 to simulate soil C pools in deeper soils (0–100 cm). Our best simulations produced global soil C pools totaling 746 and 978 Pg C for CLM4cn and DAYCENT parameterizations, respectively, compared to observational Correspondence to: W. R. Wieder, estimates of 1259 Pg C. In spite of their differences in complexity and equilibrium soil C pools, predictions of soil [email protected] C losses with warming temperatures through 2100 were strikingly similar for both models. Ultimately, CLM4cn and DAYCENT come from the same class of models that represent the turnover of soil C as a first-order decay Citation: process. While this approach may have utility in calculating steady state soil C pools, the applicability of this Wieder, W. R., J. Boehnert, and G. B. model configuration in transient simulations remains poorly evaluated. Bonan (2014), Evaluating soil biogeo- chemistry parameterizations in Earth system models with observations, Global Biogeochem. Cycles, 28, 211–222, 1. Introduction doi:10.1002/2013GB004665. Soils contain the largest terrestrial pool of carbon (C) globally [Jobbágy and Jackson, 2000; Tarnocai et al., Received 28 MAY 2013 2009], yet the response of soil C pools to environmental change remains uncertain. Driven by heterotrophic Accepted 26 JAN 2014 respiration of organic C substrates, turnover of soil organic carbon (SOC) pools is sensitive to changes in Accepted article online 3 FEB 2014 temperature, moisture, and plant productivity [Davidson and Janssens, 2006; Davidson et al., 2012; Falloon Published online 13 MAR 2014 et al., 2011]. The degree to which soils globally may serve as a source or a sink for C in the future remains an open question. This uncertainty around the response of SOC pools to environmental change remains a significant challenge for Earth system models (ESMs) [Jones et al., 2003] that are used to investigate carbon- climate feedback and make climate predictions through the 21st century and beyond. Predictions of SOC response to environmental change depend on accurately simulating extant SOC pools and also simulating the processes governing SOC stabilization and turnover in a highly heterogeneous soil environment. Thus, simulating soil biogeochemical processes at the global scale requires reasonable approximations of climate and productivity, and in some cases additional information on the soil physical and chemical environment. Given this complexity, it is not surprising that ESMs vary widely in their predictions of SOC pools and generally show poor spatial correlation with observations [Todd-Brown et al., 2013]. The Community Land Model version 4 (CLM4cn), the terrestrial component of the Community Earth System Model version 1.0 (CESM), simulates notably small SOC pools globally and has low SOC densities at high latitudes [Thornton et al., 2007; Todd-Brown et al., 2013]; soil carbon from a CLM4cn twentieth century control simulation [Lawrence et al., 2011] totals 502 Pg C globally, providing motivation for the work presented here. Small SOC pools in CLM4cn may result from input biases, such as climate or productivity, or from structural errors in simulations of the processes that regulate C turnover in soils. Global productivity in CLM4cn is high [Beer et al., 2010]; however, regionally dry soils in the arctic result in low productivity, which may account for some of CLM4cn’s low SOC pools [Lawrence et al., 2011]. Unrealistically, small SOC pools in CLM4cn may also result from rapid SOC turnover inherent in its soil biogeochemistry parameterization. Compared to observations from the Long-term Intersite Decomposition Experiment (LIDET) leaf and root litter decomposes too rapidly in CLM4cn simulations [Bonan et al., 2013]. In contrast, litter decomposition in Daily Century (DAYCENT) (and its predecessor CENTURY), a well-tested ecosystem model [Kelly et al., 1997; Parton et al., 1987, 1993, 1994], better WIEDER ET AL. ©2014. American Geophysical Union. All Rights Reserved. 211 Global Biogeochemical Cycles 10.1002/2013GB004665 matched LIDET observations [Bonan et al., 2013]. Since litter decomposition serves as the precursor to SOC formation, this suggests DAYCENT biogeochemistry may more accurately represent soil biogeochemical processes. Despite its widespread use in soil biogeochemical models, the DAYCENT soil biogeochemistry parameterization has not yet been tested at the global scale [see Schimel et al., 1994]. We continue efforts initiated with the LIDET study [Bonan et al., 2013] to evaluate soil biogeochemical models with observations at the global scale, here focusing on equilibrium SOC pools generated by CLM4cn and DAYCENT soil biogeochemical models. We focus our analysis on these two models; however, all of the ESMs used to make global climate projections employ soil biogeochemistry models with analogous structures— simulating the turnover of SOC in n soil C pools via first-order kinetics [Todd-Brown et al., 2013]. Thus, the general approach outlined here is widely applicable to a diversity of models. Using an analytical solution to find equilibrium SOC pools [Xia et al., 2012], we test how well each model replicates global SOC observations and identify input parameters important for global SOC simulations. We additionally evaluate the importance of different model structures when simulating potential effects of soil warming on SOC over the 21st century. 2. Methods Soil biogeochemical models are slow to spin-up to an equilibrium state [Thornton and Rosenbloom, 2005]; yet at their core soil biogeochemistry models are relatively simple—simulating the exponential decay of litterfall inputs and SOC pools with decay rates in n pools modulated by environmental scalars (e.g., soil temperature and moisture). To facilitate model analyses, we used an analytical solution to calculate equilibrium soil C pools for CLM4cn and DAYCENT. Both models were forced with prescribed litterfall input, soil temperature, and soil moisture. Simulations used the CLM grid, approximately 1° horizontal resolution. Model results were compared to soil carbon observations. 2.1. Analytical Solution We used an analytical solution to calculate equilibrium soil C pools modified from Xia et al. [2012, 2013]. The Xia et al. [2012, 2013] framework calculates steady state ecosystem carbon pools (vegetation, coarse woody debris, litter, and soil organic matter) from gross primary production. Our modified approach calculates coarse woody debris, litter, and soil pools from litterfall. Generally, these models are described where the carbon balance for pool i is the sum of the litter input ui to pool i and all the flows to pool i from all n pools minus the carbon turnover: n dx ÀÁ i ¼ u þ ∑ 1 À r f ξ k x À ξ k x (1) dt i ij ij j j j i i i j ¼ 1 j ≠ i Here ki is the base fractional loss for pool i, ξi is an environmental scalar that modifies the turnover rate, and ξikixi is the carbon turnover from pool i. Similarly, ξjkjxj is the turnover from donor pool j, and fij is fraction that transfers from donor pool j to receiver pool i. The fraction 1 À rij is the microbial growth efficiency, or the fraction of the carbon turnover that is assimilated into microbial biomass; the fraction rij of the flow to the receiver pool is lost as respiration. Equation (1) can be written in matrix notation, with dX ¼ BU þ AξKX (2) dt T T where, X =[x1,x2, …,xn] is a n × 1 column vector of n carbon pools; U =[u1,u2, …,um] is a m × 1 column vector of litter fluxes for m types of litter; B is a n × m litter flux partitioning matrix in which bij is the partitioning of litter flux j to pool i; A is an n × n carbon transfer matrix in which ajj = À 1 for pool j and aij =(1À rij)fij is the fraction of carbon loss from pool j entering pool i (for j ≠ i); ξ is a n × n diagonal matrix in which ξjj is the environmental scalar for pool j and all other elements are zero (ξij = 0 for j ≠ i); and K is a n × n diagonal matrix in which kjj is the base fractional loss for pool j and all other elements are zero (kij = 0 for j ≠ i). At steady state, dX/dt = 0 and the steady state equilibrium pools (Xss) are À1 Xss ¼ðÞAξK BU (3) WIEDER ET AL. ©2014. American Geophysical Union. All Rights Reserved. 212 Global Biogeochemical Cycles 10.1002/2013GB004665 The coefficients in equation (3) can vary over time with environmental conditions. However, time-mean values B, A, and ξ approximate the steady state solution for a given U [Xia et al., 2012]. 2.2. Model Descriptions Soil biogeochemistry in CLM4cn represents organic matter decomposition as a converging cascade that uses three litter pools, four soil organic matter pools, and one course woody debris (CWD) pool [Bonan et al., 2013; Thornton and Rosenbloom, 2005].
Load more

Recommended publications
  • Soil Organic Carbon Scoping Paper
    DISCUSSION PAPER Standardized GHG Accounting for Soil Organic Carbon Accrual on Non-Forest Lands: Challenges and Opportunities September 23, 2019 Discussion Paper for Soil Organic Carbon Accrual September 23, 2019 About this document This paper is meant to take stock of the current “state of play” with respect to the creation of tradable offsets to incentivize building soil carbon in non-forested soils. The document discusses the potential for developing a carbon offset protocol for soil organic carbon (SOC) accrual. The document first discusses challenges, including quantification, permanence and additionality. Then, new technologies and approaches are presented as options to overcome the challenges, and there is a short overview of how some offset programs are addressing SOC. We conclude the document with a general description of the approaches that could be used in a Reserve SOC protocol. Executive summary Soil organic carbon (SOC) sequestration practices have a high potential for GHG emission reductions, but these practices remain underutilized by carbon offset programs due to several challenges, including costs, uncertainty, permanence, and additionality. This document evaluates the potential for developing a Reserve SOC accrual protocol that is applicable to the United States, Mexico, and Canada. Potential practices would increase carbon stocks mainly in cropland and grassland, and would include conservation tillage, efficient irrigation, sustainable grazing, nutrient management, and cover-cropping. There are several technologies in use and in development to achieve certain and cost-effective quantification at varying degrees. Traditional lab-based dry combustion methods combined with bulk density tests are the gold standard for SOC quantification, but direct sampling and laboratory analysis are costly and time intensive.
    [Show full text]
  • Model Based Regional Estimates of Soil Organic Carbon Sequestration and Greenhouse Gas Mitigation Potentials from Rice Croplands in Bangladesh
    land Article Model Based Regional Estimates of Soil Organic Carbon Sequestration and Greenhouse Gas Mitigation Potentials from Rice Croplands in Bangladesh Khadiza Begum 1,*, Matthias Kuhnert 1, Jagadeesh Yeluripati 2, Stephen Ogle 3, William Parton 3, Md Abdul Kader 4,5 ID and Pete Smith 1 ID 1 Institute of Biological & Environmental Science, University of Aberdeen, 23 St Machar Drive, Aberdeen AB24 3UU, UK; [email protected] (M.K.); [email protected] (P.S.) 2 The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK; [email protected] 3 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; [email protected] (S.O.); [email protected] (W.P.) 4 Department of Soil Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh; [email protected] 5 School of Agriculture and Food Technology, University of South Pacific, Suva, Fiji * Correspondence: [email protected] or [email protected] Received: 25 June 2018; Accepted: 2 July 2018; Published: 5 July 2018 Abstract: Rice (Oryza sativa L.) is cultivated as a major crop in most Asian countries and its production is expected to increase to meet the demands of a growing population. This is expected to increase greenhouse gas (GHG) emissions from paddy rice ecosystems, unless mitigation measures are in place. It is therefore important to assess GHG mitigation potential whilst maintaining yield. Using the process-based ecosystem model DayCent, a spatial analysis was carried out in a rice harvested area in Bangladesh for the period 1996 to 2015, considering the impacts on soil organic carbon (SOC) sequestration, GHG emissions and yield under various mitigation options.
    [Show full text]
  • Agricultural Soil Carbon Credits: Making Sense of Protocols for Carbon Sequestration and Net Greenhouse Gas Removals
    Agricultural Soil Carbon Credits: Making sense of protocols for carbon sequestration and net greenhouse gas removals NATURAL CLIMATE SOLUTIONS About this report This synthesis is for federal and state We contacted each carbon registry and policymakers looking to shape public marketplace to ensure that details investments in climate mitigation presented in this report and through agricultural soil carbon credits, accompanying appendix are accurate. protocol developers, project developers This report does not address carbon and aggregators, buyers of credits and accounting outside of published others interested in learning about the protocols meant to generate verified landscape of soil carbon and net carbon credits. greenhouse gas measurement, reporting While not a focus of the report, we and verification protocols. We use the remain concerned that any end-use of term MRV broadly to encompass the carbon credits as an offset, without range of quantification activities, robust local pollution regulations, will structural considerations and perpetuate the historic and ongoing requirements intended to ensure the negative impacts of carbon trading on integrity of quantified credits. disadvantaged communities and Black, This report is based on careful review Indigenous and other communities of and synthesis of publicly available soil color. Carbon markets have enormous organic carbon MRV protocols published potential to incentivize and reward by nonprofit carbon registries and by climate progress, but markets must be private carbon crediting marketplaces. paired with a strong regulatory backing. Acknowledgements This report was supported through a gift Conservation Cropping Protocol; Miguel to Environmental Defense Fund from the Taboada who provided feedback on the High Meadows Foundation for post- FAO GSOC protocol; Radhika Moolgavkar doctoral fellowships and through the at Nori; Robin Rather, Jim Blackburn, Bezos Earth Fund.
    [Show full text]
  • Carbon Sequestration and Greenhouse Gas Mitigation Potential of Composting and Soil Amendments on California’S Rangelands
    CARBON SEQUESTRATION AND GREENHOUSE GAS MITIGATION POTENTIAL OF COMPOSTING AND SOIL AMENDMENTS ON CALIFORNIA’S RANGELANDS A Report for: California’s Fourth Climate Change Assessment Prepared By: Whendee L. Silver1, Sintana E. Vergara1, Allegra Mayer1 1 Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720. DISCLAIMER This report was prepared as the result of work sponsored by the California Natural Resources Agency. It does not necessarily represent the views of the Natural Resources Agency, its employees or the State of California. The Natural Resources Agency, the State of California, its employees, contractors and subcontractors make no warrant, express or implied, and assume no legal liability for the information in this report; nor does any party represent that the uses of this information will not infringe upon privately owned rights. This report has not been approved or disapproved by the Natural Resources Agency nor has the Natural Resources Agency passed upon the accuracy or adequacy of the information in this report. Edmund G. Brown, Jr., Governor August 2018 CCC4A-CNRA-2018-002 ACKNOWLEDGEMENTS We would like to thank several people whose input has been instrumental in this work. We are grateful to Jeff Borum and the Natural Resource Conservation Service (NRCS) for organizing the field campaign and assisting with sampling. Tom Hedt facilitated excellent discussions on the field design and preliminary sampling. Ken Oster helped in the field and also provided valuable input. Sam Grubinger, Summer Ahmed, Ken Marchus, Josh Schimel, and Alison Haddad helped in the field and the lab. This project gave many undergraduate students an opportunity to learn about soil processing (they were champions at sorting compost and roots out of the hundreds of soil samples).
    [Show full text]
  • Stoichiometrically Coupled Carbon and Nitrogen Cycling in the Microbial-Mineral Carbon Stabilization Model Version 1.0 (MIMICS-CN V1.0)
    Geosci. Model Dev., 13, 4413–4434, 2020 https://doi.org/10.5194/gmd-13-4413-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0) Emily Kyker-Snowman1, William R. Wieder2,3, Serita D. Frey1, and A. Stuart Grandy1 1Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH 03824, USA 2Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80305, USA 3Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO 80309, USA Correspondence: Emily Kyker-Snowman ([email protected]) Received: 13 November 2019 – Discussion started: 19 December 2019 Revised: 1 June 2020 – Accepted: 24 July 2020 – Published: 22 September 2020 Abstract. Explicit consideration of microbial physiology in chiometric characteristics of soil microbes, especially under soil biogeochemical models that represent coupled carbon– environmental change scenarios. nitrogen dynamics presents opportunities to deepen under- standing of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physic- 1 Introduction ochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS Soils contain the largest actively cycling terrestrial carbon with coupled C and nitrogen (N) cycling through litter, mi- (C) stocks on earth and also serve as the dominant source of crobial, and soil organic matter (SOM) pools. The model nutrients, like nitrogen (N), that are critical for maintaining was parameterized and validated against C and N data from ecosystem productivity (Gruber and Galloway, 2008; Job- the Long-Term Inter-site Decomposition Experiment Team bágy and Jackson, 2000).
    [Show full text]
  • A Moisture Function of Soil Heterotrophic Respiration That Incorporates Microscale Processes
    ARTICLE DOI: 10.1038/s41467-018-04971-6 OPEN A moisture function of soil heterotrophic respiration that incorporates microscale processes Zhifeng Yan1, Ben Bond-Lamberty 2, Katherine E. Todd-Brown3, Vanessa L. Bailey 3, SiLiang Li1, CongQiang Liu4 & Chongxuan Liu 5 Soil heterotrophic respiration (HR) is an important source of soil-to-atmosphere CO2 flux, but its response to changes in soil water content (θ) is poorly understood. Earth system models 1234567890():,; commonly use empirical moisture functions to describe the HR–θ relationship, introducing significant uncertainty in predicting CO2 flux from soils. Generalized, mechanistic models that address this uncertainty are thus urgently needed. Here we derive, test, and calibrate a novel moisture function, fm, that encapsulates primary physicochemical and biological processes controlling soil HR. We validated fm using simulation results and published experimental data, and established the quantitative relationships between parameters of fm and measurable soil properties, which enables fm to predict the HR–θ relationships for different soils across spatial scales. The fm function predicted comparable HR–θ relationships with laboratory and field measurements, and may reduce the uncertainty in predicting the response of soil organic carbon stocks to climate change compared with the empirical moisture functions currently used in Earth system models. 1 Institute of Surface-Earth System Science, Tianjin University, 300072 Tianjin, China. 2 Pacific Northwest National Laboratory-University of Maryland Joint Global Climate Change Research Institute, College Park, MD 20740, USA. 3 Pacific Northwest National Laboratory, Richland, WA 99354, USA. 4 State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, 550081 Guiyang, China.
    [Show full text]
  • Greenhouse Gas Mitigation and Accounting. In: Agroforestry
    Chapter 3 Greenhouse Gas Mitigation and Accounting Michele Schoeneberger and Grant Domke Michele Schoeneberger is research program lead and soil scientist (retired), U.S. Department of Agri­ culture (USDA), Forest Service, USDA National Agroforestry Center; Grant Domke is team leader and research forester, USDA Forest Service, Forest Inventory and Analysis. Contributors: P.K.R. Nair, Marlen Eve, Alan Franzluebbers, Christopher Woodall, Toral Patel­ Weynand, James Brandle, and William Ballesteros Possu P.K.Ramachandran Nair is the distinguished professor of agroforestry at the School of Forest Resources and Conservation, University of Florida; Marlen Eve is the national program leader for Natural Resources and Sustainable Agricultural Systems, USDA Agricultural Research Service; Alan Franzluebbers is a research plant physiologist, USDA Agricultural Research Service; Christopher Woodall is project leader and research forester, USDA Forest Service, Northern Research Station, Center for Research on Ecosystem Change; Toral Patel-Weynand is the director of Sustainable Forest Management Research, USDA Forest Service, Research and Development; James Brandle is emeritus shelterbelt professor, School of Natural Resources, University of Nebraska; William Ballesteros Possu is associate professor, agrarian sciences faculty, University of Nariño. It is well documented that forests provide significant carbon ability to offset these emissions through the use of management (C) sequestration (McKinley et al. 2011). In the 2013 U.S. practices, of which TOF-based practices and, specifically, the Department of Agriculture (USDA) Greenhouse Gas Inventory, TOF practice of agroforestry, are now included (CAST 2011, forests, urban trees, and harvested wood accounted for the Denef et al. 2011, Eagle and Olander 2012, Eve et al. 2014). majority of agricultural and forest sinks of carbon dioxide Agroforestry is the intentional integration of woody plants (CO2) (USDA-OCE 2016).
    [Show full text]
  • DAYCENT and Its Land Surface Submodel: Description and Testing
    Global and Planetary Change 19Ž. 1998 35±48 DAYCENT and its land surface submodel: description and testing William J. Parton a,), Melannie Hartman a, Dennis Ojima a, David Schimel b a Natural Resource Ecology Laboratory, Colorado State UniÕersity, Fort Collins, CO 80523, USA b Climate and Global Dynamics DiÕision, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80303, USA Received 20 September 1997; accepted 9 February 1998 Abstract A land surface submodel was developed for the daily version of the CENTURY ecosystem modelŽ. DAYCENT . The goal of DAYCENT to simulate soil N2 O, NOx , and CH4 fluxes for terrestrial ecosystems determined the structure and processes represented in the land surface model. The land surface model was set up to simulate daily dynamics of soil water and temperature from a multi-layered soil systemŽ. 0±1, 1±4, 4±15, 15±30 cm, etc. and included surface runoff and above field capacity soil water dynamics during intense rainfall events and snowmelt into frozen soils. The comparison of the simulated soil water contentŽ. 0±10 cm with observed data from four sites was quite favorable Ž squared correlation coefficientÐg 2 s0.87, 0.65, 0.86 and 0.58. and the simulated results were comparable for the soil temperature model Žr 2 s0.92 and 0.95 for minimum and maximum 10 cm soil temperatures. Detailed soil water and temperature data during snowmelt time periods and following rainfall events are needed to fully evaluate the performance of the water flow model. q 1998 Elsevier Science B.V. All rights reserved.
    [Show full text]
  • CENTURY Tutorial
    CENTURY Tutorial Supplement to CENTURY User’s Manual Bill Parton Dennis Ojima Steve Del Grosso Cindy Keough Table of Contents 1. CENTURY Model Overview 1 1.1. Introduction 1 1.2. CENTURY Model Description 1 1.3. Soil Organic Matter Model 4 1.4. Soil Water and Temperature Model 10 1.5. Plant Production and Management Model 12 1.6. Use and Testing of the CENTURY Model 15 1.7. DAYCENT Model Description 16 2. Downloading and Installing the PC Version of CENTURY 18 3. CENTURY, Associated Files, and Utility Programs 20 4. Preparing for a CENTURY Simulation 24 5. Running CENTURY and its Utility Programs 26 5.1. FILE100 27 5.1.1. Reviewing All Options 28 5.1.2. Adding an Option 28 5.1.3. Changing an Option 29 5.1.4. Changing the <site>.100 File 30 5.1.5. Deleting an Option 32 5.1.6. Comparing Options 32 5.1.7. Generating Weather Statistics 33 5.1.8. XXXX.100 Backup File 34 5.2. EVENT100 35 5.2.1. The Concept of Blocks 35 5.2.2. Defaults and Old Values 36 5.2.3. What EVENT100 Needs 37 5.2.4. Using EVENT100 37 5.2.5. Explanation of Event Commands 42 5.2.6. Explanation of System Commands 45 5.2.7. The -i Option: Reading from a Previous Schedule File 48 5.3. CENTURY 49 5.4. LIST100 50 6. Viewing CENTURY Output Listing from LIST100 51 6.1. Using a text editor 51 6.2. Using Microsoft Excel 51 6.3.
    [Show full text]
  • Fsbdev2 034643.Pdf
    Blank Back Page International Symposium on Soil Organic Matter Dynamics: Land Use, Management and Global Change July 6‐9, 2009 Cheyenne Mountain Conference Center Colorado Springs, Colorado, USA Organizing Committee Keith Paustian – Colorado State University (Chair) M. Francesca Cotrufo – Colorado State University (Vice‐Chair) Richard Conant – Colorado State University Ronald Follett – USDA/Agricultural Research Service, Fort Collins Eugene Kelly – Colorado State University Maurice Mausbach – USDA/Natural Resource Conservation Service (retired) Stephen Ogle – Colorado State University Eldor Paul – Colorado State University Gary Peterson – Colorado State University Michael Ryan – US Forest Service, Fort Collins Scientific Committee Olof Andrén – Swedish University of Agricultural Sciences, Sweden Carlos E.P. Cerri – University of Sao Paolo, Brazil Abad Chabbi – INRA‐UEFE, France Jorge Etchevers – Colegio de Postgraduados, Mexico Paul Fixen – International Plant Nutrition Institute Jürg Fuhrer – ART, Zurich, Switzerland Peter Grace – Queensland University of Technology, Australia Georg Guggenberger – Leibniz Universität Hannover, Germany Henry Janzen – Agriculture and Agrifood Canada Miko Kirschbaum – Landcare Research, New Zealand Rattan Lal – Ohio State University, USA Johannes Lehmann – Cornell University, USA Roel Merckx – Belgium Genxing Pan – Nanjing University, China Phil Sollins – Oregon State University, USA Conference Sponsors 25×’25 Carbon Management Center, Colorado Clean Energy Collaboratory Chicago Climate Exchange College
    [Show full text]
  • Sorghum Biomass Production in the Continental United States and Its Potential Impacts on Soil Organic Carbon and Nitrous Oxide Emissions
    Lawrence Berkeley National Laboratory Recent Work Title Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions Permalink https://escholarship.org/uc/item/0pm229f4 Journal GCB Bioenergy, 12(10) ISSN 1757-1693 Authors Gautam, S Mishra, U Scown, CD et al. Publication Date 2020-10-01 DOI 10.1111/gcbb.12736 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Received: 29 April 2020 | Revised: 16 June 2020 | Accepted: 24 July 2020 DOI: 10.1111/gcbb.12736 ORIGINAL RESEARCH Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions Sagar Gautam1,2 | Umakant Mishra1,2 | Corinne D. Scown2,3,4 | Yao Zhang5 1Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA Abstract 2Joint BioEnergy Institute, Lawrence National scale projections of bioenergy crop yields and their environmental impacts are Berkeley National Laboratory, Emeryville, essential to identify appropriate locations to place bioenergy crops and ensure sustainable CA, USA land use strategies. In this study, we used the process-based Daily Century (DAYCENT) 3 Energy Analysis & Environmental Impact model with site-specific environmental data to simulate sorghum (Sorghum bicolor Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emis- 4Biological Systems and Engineering sions across cultivated lands in the continental United States. The simulated rainfed Division, Lawrence Berkeley National dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotem- Laboratory, Berkeley, CA, USA 9.
    [Show full text]
  • Understanding the Daycent Model: Calibration, Sensitivity, and Identifiability Through Inverse Modeling Magdalena Necpálová University of Wisconsin–Madison
    Agronomy Publications Agronomy 4-2015 Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling Magdalena Necpálová University of Wisconsin–Madison Robert P. Anex University of Wisconsin–Madison Michael N. Fienen United States Geological Survey Stephen J. Del Grosso Colorado State University Michael J. Castellano IFoowlalo Swta tthie Usn iaverndsit ay,dd casitteionlmj@ial wasorktates.e duat: http://lib.dr.iastate.edu/agron_pubs Part of the Agricultural Science Commons, Agriculture Commons, and the Agronomy and Crop See next page for additional authors Sciences Commons The ompc lete bibliographic information for this item can be found at http://lib.dr.iastate.edu/ agron_pubs/99. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Agronomy at Iowa State University Digital Repository. It has been accepted for inclusion in Agronomy Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling Abstract The ba ility of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit ot − crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soilNO3 compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default − + model for all model predictions, except for soil NO3 and NH4 .
    [Show full text]