PhD Thesis Presentation Peter Löwe. The dissertation "Artificial Intelligence Methods in Radarmeteorology and Soil Erosion Research" discusses the assessment of potential rainfall erodibility in regard to soil erosion processes in South Africa. Knowledge-based approaches are used to derive rainfall information from weather radar data for the recording of erosivity pulses from individual rainfall events. This precipitation data is used as input for a erosivity modell consisting built out of cellular automata. The results generated by the modell are presented and discussed. Thesis Download: http://opus.bibliothek.uni-wuerzburg.de/volltexte/2004/759/

1. Artificial Intelligence Techniques applied to
Radarmeteorology
and Soil Erosion Research
Studies regarding a soil erosion parameter in South Africa
Dr. Peter Löwe
(loewe@geomancers.net)
University of Würzburg, Germany
Geography Department

2. Overview
•Topic, theory and
previous projects in the field
•Hypothesis and approach
I Technical framework
II Modelling
III Results

3. Introducing the problem
Soil erosion is the process of Terrain Vegetation
soil destruction, a natural process,
Climate
which can be initiated or amplified by Soils Humans
human land management. ... Soil
erosion can deminish the agricultural Control Enhance
yield significantly.
Transportation Start
(Scheffer Schachtschabel, 1992) Wind Water SOIL EROSION
In addition to human use of the
Earth's surface, climate is a key
factor. It provides a means of
transportation for soil material to be
carried away.
How can these processes
be modelled ?

4. Modelling soil erosion
Universal Soil Loss Equation (USLE): Core problem: When-How much-Where?
Soil Erosion as a function of
When does how much soil loss occur
where?
Erosivity Erodibility
When does how much erosivity occur where ?
When does how much rain fall where ?
Physical
Rainfall properties
Management
Land Plant
Energy [EI30] Management Management
Aim of the Dissertation
Providing detailed information
about erosivity by relying on the
available data sources:
A= R * K * LS * P * C
How erosive is rain
When ? How much ? Where ?

5. Theory and precessor work
The DFG-project „The meridional and planetary differentiation of soil
erosion in southern Africa and its causes“ postulates the Rainfall
Erosivity Index (REI) as a locally „superior“ erosivity factor.
REI-Components:
• Quantity
• Energy
• Intensity
• Structure
Approach: Spatial
interpolation (1,22 Mio
km²) from ca. 120 rain
gauge data sets [approx.
10,000 km² per gauge]
Results: Monthly- and
annual maps of erosivity
totals for South Africa.

6. Problems with the
database
When ? Precipitation data is recorded in 5-
minute resolution: OK
How much ? Index values are calculated on these
time series: OK
Where ? Results from singular rain 100 * 100 km
gauges are spatially interpolated ?
Example: Rain gauge from the
Hypothesis
Liebenbergvlei network, Station: Bethlehem
Airport, Tipping Gauge Type
The described REI-processing does not capture the real-world distribution
of convective, small-scaled erosivity „pulses“ - it is only affecting the
produced maps at random.
There is a sampling and interpolation-
problem if rain gauge data is used

7. Alternative:weather radar
Data has been recorded since 1995 by the SAWS.
•temporal resolution: 5 minutes OK
•spatial resolution: 1km², full area coverage
OK
•Data: Reflectivity values [dBZ]
three-dimensional distribution of
hydrometeors in the lower
tropossphere and their
sizes.
•Products: derived quantitative Usable ?
precipitation values [mm/h]
Radarpluviograms
For the upcoming discussion, only data from a single
radar station is being used: The MRL-5 in the
Liebenbergvlei catchment (oldest station available, lots of
expert knowledge available, 10cm S-band und 3cm X-band).

8. Radarpluviograms daily precipitation totals + spatial data
Usability to derive REI-
values:
•When ? daily totals (24h)
•How much? Sums, no intensities
•Where? Inhomogenous content
• missing meta data
• artefacts
• maps instead of data
Pluviogram-products can be properly
judged if radarmeteorological knowledge
is available, otherwise they are
problematic

9. Idea and approach
Hypothesis: There are small, temporally fluctuating peaks of erosiviy due
to the convective weather situation, yet they are still uncharted.
A sufficiently high temporal (When ?) and spatial data coverage (Where ?),
is needed, and also a measure of confidence for the data content
(How much ?).
To answer „When-How much-Where“ the radar reflectivity
products must be accessed and processed.
Technical
Modelling Results
Framework
Geoinformatics,
Information- Analysis and •Verification
logistics,
encoding of the •Validation
Remote sensing,
REI. •Results
(D)AI,
Radarmeteorology
I II III

10. I Overview: Framework
Structure and components
Data
Data base
Import
Geo- Expert
information- systems
system
Multi Agent-
Information
simulation
logistics
(REI)
The development of an expert system shell embedded within the
GIS, a radar data import module, a simulation environment and the
logistic processes was based on Open Source/Free Software using
GRASS GIS, PostgreSQL, CLIPS, CAPE and the expert system
shell toolkit D3 of the U. of Würzburg, Germany (Chair for A.I.).

11. I II REI-Modelling
Precipitation- REI-
data stream data stream Maps
of „radar rain“
For each spatial cell which has Init
State 1 Index-
radar coverage, a „virtual rain D Hibernate
value
gauge“ needs to be simulated,
which will derive REI-values
P D
according to its individual input
data stream.
REI-
D Cell-
For this reason, agent technology State 2 State 3
is used, as each „gauge-agent“ Store Pause Agent
must keep its‘ own record of P
previous precipitation events. P: Precipitation
P
D: Dry

12. Data flow
I II
Reflectivity maps
What type of X
weather occurs X X
when, where? „same rain
X X X X
X X XX everywhere“
X X
SSS
REI-Model Erosivity
XPS Z-R maps
Constrat 1
a
b -
c 4
d 2
Rainfall maps, Pluviogram
When does how much rain fall Where does erosivity potential
where ? occur ?

13. I II III Time series
The mapping of REI values by the Cell-Agents shows a pattern of „erosivity
shadows“, trailing behind the tracks of precipitation zones. By calculating sums
for each raster cell maps of daily totals can be created.
24h total
Reflectivity
Σ
Reflectivity
16:18:50 Hours 16:43:30 Hours 16:59:56 Hours 24h total
REI
Σ
REI-Erosivity

14. I II III Daily totals
Data set: 15. Dezember 1998
Reflectivities Precipitation REI-Erosivity
Stormcell-tracks
In the following, only the values of
the northern half of the MRL-5
coverage area are shown.

15. 3-D Visualization Reflectivity and Precipitation
Reflectivity total
Radar
Precipitation total
? ?
•Steps/Etages are artefacts of the radar processing.
•Decreasing reflectivity totals with increasing range/distance are caused by the rising „radar
scan horizon“.
How does this affect the REI-values ?

16. 3-D Visualization REI- values und Precipitation
Altitude: REI values, Color: Precipitation
The „rings“ also affect the REI-values,
Altitude and Color: Precipitation but the amplitude of the erosive events
is significantly higher.

17. REI-Totals display local erosivity-pulses
Rainfall Erosivity
Index (REI)
•Quantity
•Energy
•Intensity
•Structure
Despite overall decreasing precipitation-
totals with increasing range are qualitative
REI-pulses recorded.

18. Conclusion I II III
Hypothesis is verified: The use of radar data shows powerful
localized dynamics of convective weather phenomena within the
test region. It is possible to infer strongly localized erosivity
pulses.
The proposed full spatial coverage of occurring erosivity pulses
in the given example just by means of interpolating from four
rain gauges of the national precipitation network is not realistic
(200*200 km).
When does how much erosivity occur
where ?
•Any kind of erosivity modell (REI, EI30,
KE>25, etc.) can be simulated, using the
developed software-framework (how much ?)
•qualitative answers with full spatial coverage
by Radar Data - GIS integration(where ?,when ?)

19. fin
have a nice day

20. I can see clearly now,
the rain has gone.
I can see all obstacles
in my way
[Liza Minelli]
Thank you for your attention