Carbon losses from all soils across England and Wales 1978-2003 Pat Bellamy, Peter Loveland, Ian Bradley, Murray Lark (Rothamsted), Guy Kirk

Slides:



Advertisements
Similar presentations
Attribute values are classified, e.g. DR = Depth to rock. S = Shallow ( < 40 cm) M = Moderate ( cm) D = Deep ( cm) V = Very deep ( > 120.
Advertisements

1 Stocks of biomass / C soil organic matter Expert meeting on land use and Ecosystem accounting 18./
Biotic and Abiotic factors that control soil development
Developing a model of methane (and other GHG) emissions from highly organic soils Jo Smith & Helen Flynn School of Biological Sciences University of Aberdeen.
VB Standortcharakterisierung (Cluster B: soil) Wulf Amelung, Kurt Heil, Andreas Pohlmeier, Stefan Pätzold, Urs Schmidhalter, Lutz Weihermüller, Gerd Welp.
Soil Erosion Estimation TSM 352 Land and Water Management Systems.
Factors influencing Soil Formation
Soil formation begins with weathering of bedrock
Soil Mapping and Erosion
Magnitude and Spatial Distribution of Uncertainty in Ecosystem Production and Biomass of Amazonia Caused by Vegetation Characteristics Christopher Potter.
Soil CO 2 Efflux from a Subalpine Catchment Diego A. Riveros-Iregui 1, Brian L. McGlynn 1, Vincent J. Pacific 1, Howard E. Epstein 2, Daniel L. Welsch,
The Effects of Different Resolution DEMs in Determining Overland Flow Regimes Stacy L. Hutchinson 1, J.M. Shawn Hutchinson 2, Ik-Jae Kim 1, and Philip.
Management impacts on the C balance in agricultural ecosystems Jean-François Soussana 1 Martin Wattenbach 2, Pete Smith 2 1. INRA, Clermont-Ferrand, France.
A FRAMEWORK FOR ASSESSING THE PERFORMANCE OF DWM AT LARGE SCALE MOHAMED A. YOUSSEF and R. WAYNE SKAGGS 1 By.
WFM 6202: Remote Sensing and GIS in Water Management © Dr. Akm Saiful IslamDr. Akm Saiful Islam WFM 6202: Remote Sensing and GIS in Water Management Akm.
Processes that break down rock.  Rock is broken apart but not changed chemically.
An introduction to the monitoring of forestry carbon sequestration projects Developing Forestry and Bioenergy Projects within CDM Ecuador March, 2004 Igino.
Nutrient and sediment runoff are major contributors to non-point source pollution of Louisiana bayous. Yet the dynamics of runoff are often site specific.
Towards participatory approaches to a Multiscale European Soil Information System Nicola Filippi, Panagos Panos, Borut Vrscaj EUROPEAN COMMISSION JOINT.
PALMS: Precision Agricultural-Landscape Modeling System Precision modeling to provide decision support for farmers PALMS is software designed to provide.
Ecosystems A small – scale study of the school grounds.
Results of forest soil inventory implemented in within the scope of the demonstration project BioSoil Soil stability in ecologically and socially.
Factors affecting soil Temperature Rainfall Chemicals and minerals in the soil Soil physical and chemical characteristics vary by location Soil analyses.
Earth’s Surface is Constantly Changing
Soils CharacteristicsTexture Soil Profile Soil Types Threats to Soil.
Intro. To GIS Lecture 9 Terrain Analysis April 24 th, 2013.
Igneous Sedimentary Metamorphic Rock Very slowly weathered minerals (e.g., quartz, muscovite) Slowly weathered minerals (e.g., feldspars, biotite) Easily.
Models in GIS A model is a description of reality It may be: Dynamic orStatic Dynamic spatial models e.g., hydrologic flow Static spatial models (or point.
An important product of Weathering.
Soil temperature response to global warming: implications for carbon content from thawing permafrost soils in North America Dominik Wisser 1, Sergei Marchenko.
Oak Hickory on Coarse Textured Kamic Soils Stinchfield Woods, MI Jennifer Austin Joshua Berk Knox Erin Uloth Jennifer Dowdell Presentation for Soil Properties.
Soil Movement in West Virginia Watersheds A GIS Assessment Greg Hamons Dr. Michael Strager Dr. Jingxin Wang.
What is Soil? T Webb HHS. What is Soil? - humus: biotic community - organic/biological materials - minerals - clays and silts - “dirt” * contains air.
Slide 1 Marc Kennedy, Clive Anderson, Anthony O’Hagan, Mark Lomas, Ian Woodward, Andreas Heinemayer and John Paul Gosling Uncertainty in environmental.
CO 2 - Net Ecosystem Exchange and the Global Carbon Exchange Question Soil respiration chamber at College Woods near Durham New Hampshire. (Complex Systems.
17 May 2007RSS Kent Local Group1 Quantifying uncertainty in the UK carbon flux Tony O’Hagan CTCD, Sheffield.
 Define terms related to natural resources.  Explain why conservation of natural resources is important.  Identify major components of soil.  Identify.
Soils. Soil Weathering and erosion transports materials across Earth’s surface Weathering and erosion transports materials across Earth’s surface The.
Dru Yates Learning Objectives List and describe the 5 factors of soil formation List and describe the 4 soil forming processes.
The Soil Resource Presentation for Harvest Hastings April 10/2014.
ELECTRICAL RESISTIVITY SOUNDING TO STUDY WATER CONTENT DISTRIBUTION IN HETEROGENEOUS SOILS 1 University of Maryland, College Park MD; 2 BA/ANRI/EMSL, USDA-ARS,
Slide 1 Marc Kennedy, Clive Anderson, Anthony O’Hagan, Mark Lomas, Ian Woodward, Andreas Heinemayer and John Paul Gosling Quantifying uncertainty in the.
Page 1© Crown copyright 2004 Meteorological Inputs Groundwater Workshop, Birmingham Murray Dale, 4/11/04.
BOT / GEOG / GEOL 4111 / Field data collection Visiting and characterizing representative sites Used for classification (training data), information.
Lecture 6 Raster data. Raster layers It’s all cells.
Understanding Soil.
Soils & Soil Formation-The Results of Weathering
Soil Section 5.2.
P B Hunukumbura1 S B Weerakoon1
Soil, Pedology (an introduction). Aim: To become aware of how soil is formed and various soil properties. Learning outcomes: (C) To sequence a soil profile.
Mapping of soil moisture content by SWAT and GIS programming Yuri Kim Jessica Jahnke GEOG 593.
Carbon Dynamics in Coarse Woody Debris Pools at the Tapajos National Forest in Brazil Hudson Silva Patrick Crill Michael Keller.
Jack
Soils and Growing Media
Protocols for Mapping Soil Salinity at Field Scale: EC a Survey Considerations D.L. Corwin 1 and S.M. Lesch 2 1 USDA-ARS, U.S. Salinity Laboratory Riverside,
Marc Kennedy, Tony O’Hagan, Clive Anderson,
Raster Analysis Ming-Chun Lee.
Determining Agricultural Soil Carbon Stock Changes in Canada
Jili Qu Department of Environmental and Architectural College
Statistical surfaces: DEM’s
Problems with Vector Overlay Analysis (esp. Polygon)
Figure 1. Spatial distribution of pinyon-juniper and ponderosa pine forests is shown for the southwestern United States. Red dots indicate location of.
Soil An interface in the Earth system, a boundary between different systems (biosphere, lithosphere, atmosphere). Soil is a combination of mineral matter,
Corn Soybean Wheat Overview: Methods The challenge:
What is Soil? T Webb HHS.
Spatial interpolation
Interpolating Surfaces
Biomass and Soil Moisture simulation and assimilation over Hungary
Soil organic carbon (SOC) can significantly influence key soil functional properties and improve soil quality by increasing water holding capacity, reducing.
Presentation transcript:

Carbon losses from all soils across England and Wales Pat Bellamy, Peter Loveland, Ian Bradley, Murray Lark (Rothamsted), Guy Kirk Nature 437, (2005)

1:250,000 scale 1:50,000 scale Availability of Soil Surveys in Europe, 2000

The National Soil Inventory Unbiased inventory of soil resources across England & Wales at intersects of 5 km x 5 km grid  Whole of E&W sampled in (5,662 sites)  Approx. 40% of sites re- sampled: agricultural , non-agricultural  Organic carbon content of cm soil (C org ) measured  Sampling scheme designed to detect changes of ± 2 g kg -1

Checks on methods – accuracy of relocation  Six surveyors instructed to revisit 10 sites following original protocols  Accuracy better than 20 m on enclosed land, 50 m on open land  Locations recorded with GPS; distance from target measured subsequently  Given the variability of C org at this scale, this is adequate

Checks on methods – consistency of lab. analyses  Approx. 10% of archived soils from original sampling re-analysed in 2003  Good agreement between original and re-analysed values with no systematic deviation (slope of reduced major axis correlation = 1.05, concordance correlation = 0.93)

Original soil organic carbon contents (1978/83)

Original soil organic carbon contents (1978/83) and rates of change

Rates of change - grouped by (a) soil type, (b) land use

Rates of change - grouped by original C org

Estimated changes in carbon stocks across England & Wales (and UK) Net rate of change in England & Wales = Mt yr -1 Net rate of change in UK ≈ x UK / E&W topsoil OC stock ≈ -13 Mt yr -1

Changes in carbon stocks across E&W – grouped by land use

Dynamics of soil organic carbon (SOC) CO 2, CH 4 Carbon immobilization Plant debris (high temperature, rainfall, land use) Leached OC Carbon accumulation > < Carbon loss SOC ‘pools’ fast intermediate slow Carbon mobilization (Fe, Al, clay association) input + immobilizationmobilization + output After Schultze, E.D. & Freibauer A. Nature 437, (2005)

Modelling soil carbon dynamics CO 2 Fast POC Slow POC Fast SOC (C 1 ) Intermediate SOC (C 2 ) Slow SOC (C 3 ) CO 2 decomp. rate = k 1 C 1 decomp. rate = k 2 C 2 decomp. rate = k 3 C 3 SOIL POOLS PLANT DEBRIS Rate constants (k i ) vary with soil temperature (T), moisture (θ), texture (t): k i = f(T) f(θ) f(t) k i 0

Changes in carbon stocks across E&W – grouped by soil type

Most important soil types Surface water gleys Slowly permeable, seasonally wet Podzolic soils Acid sands or loams with leached organic matter & sesquioxides, wet or dry Ah Ea Bs Bh BC&Bs BC&Ea Peat soils Permanently or seasonally wet

Modelling soil carbon dynamics CO 2 Fast plant OC Slow plant OC Fast SOC (C 1 ) decomp. rate = k 1 C 1 Intermediate SOC (C 2 ) decomp. rate = k 2 C 2 Slow SOC (C 3 ) decomp. rate = k 3 C 3 CO 2 Rate constants (k i ) vary with soil temperature (T), moisture (θ), texture (t): k i = f(T) f(θ) f(t) k i 0 SOIL POOLS PLANT DEBRIS

Soil carbon map of Europe Data sources Soil type Land cover Temperature Rasterization Spatial layers derived by rasterization of a Triangulated Irregular Network (TIN) model with weighted distance interpolation Soil type Temperature

Future research  Third sampling of NSI -plus carbon fluxes (to air and water) -plus additional data  Modelling -statistical -semi-empirical -predictive  Similar studies elsewhere