Disputation_VN3_short

Modelling long-term, large-scale sediment dynamics in an
Earth System Model framework
- PhD Disputation -
Victoria Naipal
12.01.2016
1st Evaluator: Prof. Dr. Martin Claußen
2nd Evaluator: Dr. Christian Reick

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INTRODUCTION SOIL EROSION SOIL REDISTRIBUTION CARBON & NUTRIENTS CONCLUSIONS
“A nation that destroys its soils destroys itself” – U.S. President Franklin D. Roosevelt
Soil erosion and land use change during the
last millennium for the Rhine catchmentSevere soil erosion on agricultural land
In Ethiopia
Source: www.mikegoldwater.com
Soil erosion and land use change
Bare
Grass
Forest
Crop + pasture

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Missing soil erosion related carbon exchange fluxes
Source: Ciais et al., 2014

Knowledge gaps
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 Large uncertainty in global soil erosion rates: 20 - 200 Pg y-1 (Doetterl et al, 2012)
 Sediment deposition and transport fluxes are unknown on the global scale
(Hoffmann et al., 2013)
 The effect of soil erosion and sediment dynamics on global biogeochemical
cycles is unknown (Quinton et al., 2010)
 Earth System Models (ESMs) ignore erosion and sediment dynamics and
miss an important aspect of the coupling between land and the ocean
(Van Oost et al., 2012)
 This hampers the quantification of the anthropogenic impact on the
biogeochemical cycles (Regnier et al., 2013)

Modelling soil erosion and sediment dynamics
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Outline
Part 1
Improvement and validation of an existing soil erosion model
Part 2
Development and validation of a new global sediment budget model
Part 3
Possible impacts of soil erosion on the biogeochemical cycles
Are the effects of soil erosion and associated sediment dynamics on the
biogeochemical cycles globally significant?

Modelling soil erosion on a global scale
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Objective
To develop a global soil erosion model to quantify erosion rates for present
day and for the last millennium.
Research Questions
1. How can we model local scale soil erosion on the global scale?
2. Can the global soil erosion rates be reproduced with data from ESMs?

The Universal Soil Loss Equation (RUSLE) model (Renard et al., 1997)
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 Average annual soil erosion rate (t ha-1 y-1) =
Topography (S) * Rainfall erosivity (R) * Land cover (C) * Soil erodibility (K)
* support practice (P) * slope-length (L)
Naipal et al., 2015 (GMD)

The adjusted RUSLE model (Naipal et al., 2015)
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1. Improvement of the S factor
• Slope is poorly estimated from coarse resolution global digital
elevation models (DEMs)
• The slope can be upscaled to 150 m using the fractal method
2. Improvement of the R factor
• Multiple linear regression approach
R = f(P)  R = f(P, z, I)
• Climate zones of the Köppen-Geiger climate classification
𝑆 = 𝛼𝑑1−𝐷
S = slope
D = grid resolution
α, D = fractal parameters
P = total yearly precipitation
z = mean elevation
I = precipitation intensity index

Experimental setup with the adj. RUSLE model
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Simulation using data from observational datasets
• Climate: GPCC (0.25o), CLIMDEX (2.5o), Köppen-Geiger classification (0.083o)
• Land cover and land use: GIMMS (0.25o), MODIS (0.25o)
• Topography: GTOPO (0.0083o~1 km)
• Soil: GSCE and HWSD (0.0083o~1 km)
Simulations using data from ESMs (CMIP5)
• Models: MPI-ESM (1.875o), IPSL-CM5A (3.75o), CCSM4 (1.25o), MIROC-ESM
and bcc-csm1 (2.81o)
• Climate and land cover/use data from the models
• Topography and soil data from observational datasets

Present-day global soil erosion rates - adjusted RUSLE model
(Naipal et al., 2015)
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Mean: 6.5 t ha-1 y-1
Uncertainty mean: 5.3 -15 t ha-1 y-1

Present-day erosion rates: Model input vs. observation input
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Simulation with model data
Simulation with observational data

Summary
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1. How can we model local scale soil erosion on the global scale?
• The adjusted RUSLE model reproduces present-day soil erosion rates
for Europe and the USA
2. Can the global soil erosion rates be reproduced with data from ESMs?
• Soil erosion rates derived from observational data can be reproduced
using CMIP5 data, however, there is some uncertainty
I made the RUSLE model applicable on a coarse resolution on the
global scale, however, the model is very sensitive to the climate and
land cover data from ESMs
Conclusion

Modelling sediment dynamics on a global scale
12/28
Objective
To develop a global model for sediment dynamics to quantify long-term
changes in sediment fluxes
Research Questions
1. How can we model long-term large-scale sediment dynamics?
2. How did sediment storage change during the last millennium and what
were the main drivers?

The new sediment budget model
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Flow routing scheme
Naipal et al., 2015 (Esurf, under revision)
Erosion (E(t)) = S * R * C * K
Ma(t+1) = Ma(t) + deposition fraction( fa(t+1) ) * E(t+1) – Loss ( L(t) )
L (t) = Ma(t) / residence time (τ)
Mc(t+1) = Mc(t) + deposition fraction( fc(t+1) ) * E(t+1)

Experimental setup for simulating sediment budgets during the
last millennium (850 – 2005 AD)
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 Case studies:
• Rhine catchment
• Global
 Climate and land cover/use data from MPI-ESM-LR (last millennium +
historical experiments)
 Equilibrium simulation with mean climate and landcover conditions from
850-950 AD
 Transient simulations:
1. CC+LUC: Climate and land use change
2. CC: Climate change only (land use from 850AD)
3. LUC: Land use change only (climate from 850AD)
 Change in sediment storage during the transient simulation

The Rhine catchment
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Rhine catchment validation: Scaling behavior
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𝐷𝑎𝑡𝑎: 𝑆𝑡𝑜𝑟𝑎𝑔𝑒~𝐵𝑎𝑠𝑖𝑛 𝑎𝑟𝑒𝑎1.23±0.06
𝐷𝑎𝑡𝑎: 𝑆𝑡𝑜𝑟𝑎𝑔𝑒~𝐵𝑎𝑠𝑖𝑛 𝑎𝑟𝑒𝑎1.06±0.07
For floodplains:
For hillslopes:
𝑀𝑜𝑑𝑒𝑙: 𝑆𝑡𝑜𝑟𝑎𝑔𝑒~𝐵𝑎𝑠𝑖𝑛 𝑎𝑟𝑒𝑎1.2±0.04
𝑀𝑜𝑑𝑒𝑙: 𝑆𝑡𝑜𝑟𝑎𝑔𝑒~𝐵𝑎𝑠𝑖𝑛 𝑎𝑟𝑒𝑎1.05±0.06

Rhine catchment validation: Millennial sediment storage
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Observations versus model for Rhine sub-catchments
1:1 line
trend line
rmse=3.58

Global: Millennial sediment storage
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Global: Most sediment stored on hillslopes
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Global validation: Modelled vs. observed sediment yield
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trend line
1:1 line
* Observed yields from Land2Sea database
Peuker-Ehrenbrink (2009)
rmse= 252

Global: Land use as the main contributor to sediment fluxes
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Simulation I
CC + LUC
ΔM = 6526 Gt
Simulation II
CC
ΔM = 5483 Gt
ΔM = 6658 Gt
Simulation III
LUC
Ganges
Amazon
Yellow
Nile
Mississippi

Summary
22/28
1. How can we model long-term large-scale sediment dynamics?
• The new sediment budget model reproduces the sediment storage
change in the Rhine catchment and sediment yields globally
2. How did sediment storage change during the last millennium and what
were the main drivers?
• Sediment storage increased significantly during the last millennium for
different global catchments, mainly due to land use change
Conclusion
I developed a globally applicable sediment budget model, which shows
that land use change is the main driver behind the change in sediment
storage during the last millennium

Laterally displaced carbon and nutrients
23/28
Objective
To estimate the effect of soil erosion on the lateral fluxes of carbon and
nutrients
Research Questions
1. How large are present-day lateral fluxes of carbon and nutrients due to soil
erosion only ?
2. How much of the eroded carbon and nutrients is exported by rivers?

Present-day eroded soil organic carbon (SOC)
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SOC erosion rate (Esoc) = E * area-weighted SOC%
g m-2 y-1
Esoc flux: 5.1 Pg C y-1  Esoc emissions: 1.0 Pg C y-1 ~ 13% of
fossil fuel emissions: 7.8 Pg C y-1
 River flux: 3% of Esoc

g m-2 y-1
Present-day eroded nitrogen
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N erosion rate (En) = E * area-weighted N%
En flux: 336 Tg N y-1 ~ fertilizer use + fixation + deposition: 341 Tg N y-1
 River flux: 4% of En

Present-day eroded phosphorus
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P erosion rate (Ep) = E * area-weighted P%
g m-2 y-1
Ep flux: 94 Tg P y-1 > fertilizer use + weathering: 50 Tg P y-1
 River flux: 7% of Ep

Summary
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1. How large are present-day lateral fluxes of carbon and nutrients due to
soil erosion only ?
• Soil erosion results in significant fluxes of carbon and nutrients
globally
2. How much of the eroded carbon and nutrients is exported by rivers?
• Only a small part of the mobilized carbon and nutrients is exported by
rivers
I show that soil erosion and associated sediment dynamics should not
be neglected in studying the global biogeochemical cycles
Conclusion

 I developed the first dynamical global sediment budget model that
reproduces the global features of sediment dynamics
 I show that the effects of soil erosion and associated sediment
dynamics on the biogeochemical cycles are globally significant and
should not be neglected
Overall Conclusions
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 Implementing the sediment budget model in MPI-ESM and other ESMs
 Coupling the sediment budget model with the biogeochemical
components of ESMs
 Include other types of soil erosion: landslides, gullying, glacial erosion
Outlook

Disputation_VN3_short

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