The document introduces JAMS (Jena Adaptable Modelling System), an open-source modelling framework for developing integrated eco-hydrological simulation models. JAMS allows for modular construction of models from reusable components. It includes components and models for hydrological processes, nutrient transport, glaciers, and more. JAMS aims to provide flexible, scalable and easy to use tools to support environmental management through integrated modelling of land and water systems.

1. JAMS Modelling System
open-source eco-hydrological simulation models
Part 1: Introduction
Sven Kralisch
Geographic Information Science
Institute for Geography

2. Jena…?

3. Core Water Research Areas
3
Software
Development
•Environmental
Modelling Software
Frameworks
•Simulation
Components & Models
•High-performance
Computation
Applied Modelling
•Quantitative Hydrology
•Landslide susceptibility,
hazard and risk
•Cryosphere
•Nutrient Transport &
Agriculture
•Sensitivity & Uncertaity
Analysis
Monitoring
•Remote Sensing
•Soil Moisture
Measurement
•Water Quality
(Nutrients) & Water
Quantity
•Weather - Climate

4. 4
Applied Research
• Catchment Hydrology
• Nutrients
• Landslides & Erosion
• Glaciers & Permafrost
• Irrigation & Salinity
• Climate/Landuse Change
Impact Assessment
• Information Management
• Capacitiy Building

5. Motivation
5

6. • Pressures on River Basins
– Overexploitation & pollution
(population growth, deforestation, intense
agriculture)
– Climate Change
• Impacts
– Degradation of land and water
resources (e.g. soil, wetlands) and
hydrological ecosystem services
– Floods, droughts, water scarcity, water
pollution, soil erosion, landslides, …
Background
6

7. …to provide knowledge-based support for
integrated land and water resources management
Objective
1. Understand eco-hydrological
processes and their interactions
2. Create computer models of
integrated eco-hydrological systems
3. Evaluate scenarios and assess their
impacts using computer models
7

8. • Data & information management
• Model/data integration
• Modelling of new processes
• Big data processing
• Complex workflows
• Communication of results
Methodological Challenges
8

9. Data
Management
Technical Solutions
Remote
Sensing
Model
design
&
application
Geodata
processing
9

10. JAMS Introduction
10

11. • Objectives:
– Integrated simulation of environmental systems
– Modular, problem-tailored construction of models
• Problems:
– Constraints regarding spatial / temporal scales
– Compatibility of simulation procedures
– Legacy software problems: formats, licenses, …
Where it started…
11

12. Ready-to-use
components
and models
Intuitive &
easy to use
Tools &
graphical
interfaces
Flexible &
scalable
Free & Open
(LGPL)
JAMS Modelling System
http://jams.uni-jena.de
12

13. The Modelling Workflow @
Design
Prepare data
Parameterize
Run
Calibrate
Analyze
Library Model
Design
Prepare data
Parameterize
Run
Calibrate
Analyze
Design
Prepare data
Parameterize
Run
Calibrate
Analyze
13

14. JAMS System Layout
Expert Knowledge
Model definition
Component
repository
System core
GUI
Model
•Creation
•Calibration
•Analysis
Runtime system
•Model setup & execution
•Process communication
•Data I/O
API
•Data types
•Common functions
•Data I/O interfaces
Data
•Management
•Exploration
•Visualization
14

15. Type 1: Component
• Properties:
– parameter (P) and initial state (X0)
– inner state (X)
– Slots for input (I) and
output (O) data
• Behavior:
– calculate output and new state from input and old state
• Purpose:
– physical process simulation
– data I/O, aggregation, …
– visualization
Model Building Blocks
I O
X
X0
P
15

16. Type 2: Context
• Are containers for other (child) components
• Take control of
– child component execution (e.g. iterative, conditional)
– data exchange between child components
• Examples:
Model Building Blocks
Component
Context
Sequence
Selector
Iterator
n-times
16

17. • Contexts control spatial & temporal iteration
Space & Time?
Space: discrete modelling units
(e.g. rasters or polygons)
Time: discrete points in time
(e.g. time interval + step size)
Start: 01/11/1980
End: 31/10/2000
Step: 1 day
17

18. Discrete space-time simulation in JAMS
Generic Model Layout
Temporal Context
Time
Spatial Context
Space
Process simulation &
data processing, e.g.
• Radiation
• ET & Interception
• Snow processes
• Soil infiltration,
storage, percolation
• Lateral flow
• Data aggregation
• …
Spatial iteration
Temporal iteration
18

19. 19
Model Layout in JAMS
Spatial iteration
Temporal iteration
Model

20. JAMS Models
20

21. • JAMS comes with a rich set of components which form the basis
for the J2000 hydrological model family:
– Quantitative hydrology daily/monthly (J2000/J2000g)
– Nutrient transport (J2000-S)
– Glacier processes (J2000-Glacier)
– Floodplain inundation (J2000-Flood)
• JAMS models can be easily adapted to specific needs
– Lakes and reservoirs
– Irrigation
– Soil salinity and soil erosion
– Urban/peri-urban influences (sewer systems, rain water storage, sealing)
Component Library
21

22. • Hydrological model
• Process-oriented, timed-
event type
• Spatially distributed (HRUs)
• Spatio-temporal scope:
– lower meso-scale to lower
macro-scale catchments
(~101 – 105 km²)
– timed event modelling in
hourly, daily or monthly
time steps
(Krause, 2001)
JAMS Models: J2000
Delineation of
Hydrological Response Units (HRUs)
22
(Flügel, 1996)
(Bongartz, 2001)
GIS
Overlay
Topography
Landuse, land-cover
Soil-types
Hydrogeology

23. J2000 Process Simulation
Driving data: P, T, …
ET
Interception Snow
Unsaturated zone
Saturated zone
Upper zone
Lower zone
Additional drivers: Radiation …
Runoff
Surface RO
Interflow 1
Interflow 2
Baseflow
Infiltration DPS
MPS LPS
MPS LPS
MPS LPS
1. Process simulation on each HRU 2. Simulation of routing processes
between HRUs/river reaches
23
HRU 1
HRU 4
HRU 2 HRU 3
HRU 5
HRU 6
Reach 1 Reach 3
Reach 2

24. • Model context: reading model
entities (HRU and reach
elements)
• Temporal context: iteration in
time, reading temporal input data
(e.g. climate) and aggregating/
writing result data (e.g. runoff)
• Spatial context: iteration in space
(HRUs/reaches), simulation of
hydrological processes at each
point in time/space (e.g.
interpolation of climate data, ET
and snow simulation)
J2000 Model in JAMS
Temporal
Context
Model
Context
Spatial
Context
Climate data
Interpolation
Potential ET
Snow
Radiation
Soil Water
Groundwater
Spatial data input: model entities with landuse, soil,
geology, topography
Climate data input: P, Tmin, Tavg, Tmax,
sunshine, windspeed, rel. humidity
Result output: act. ET, runoff, …
24

25. • Nitrate model based on
– SWAT (Arnold et al., 1998)
– J2000 (Krause, 2001)
• J2000-S = J2000 + SWAT processes:
– soil temperature, soil nitrate balance,
plant growth, land-use management,
groundwater nitrate accounting
• Spatially distributed (HRUs)
• Spatio-temporal scope:
– lower to upper meso-scale catchments
(~101 – 104 km²)
– timed event modelling in daily steps
(Fink et al., 2007)
JAMS Models: J2000-S
N
observed
N
modelled
(SWAT)
N
modelled
(J2K-S)
Spatial resolution of N load
25

26. J2000-S Process Simulation
Driving data: P, T, …
ET
Interception Snow
Unsaturated zone
Saturated zone
Upper zone
Lower zone
Additional drivers: Radiation …
Runoff
Surface RO
Interflow 1
Interflow 2
Baseflow
Infiltration DPS
MPS LPS
MPS LPS
MPS LPS
N
N
N
N
Soil Temperature
Module
Plant Growth
Module
Biomass
Rooting depth
LAI
Landuse
Management
Module
Fertilization
Tillage
Harvest
Soil Nitrogen
Module
Nitrification
Denitrification
Volatilisation
Plant uptake
26

27. • Core structure of the J2000
model
• Additional components for
– agricultural management (tilling,
fertilizing, harvesting, …)
– plant growth
– soil temperature
– soil nitrogen balance
J2000-S Model in JAMS
^^
Temporal
Context
Model
Context
Spatial
Context
Spatial data input: model entities with landuse, soil,
geology, topography
Climate data
Interpolation
Potential ET
Snow
Radiation
Soil Water
Groundwater
Routing
Management
Soil Temp.
Soil Nitrogen
Plant Growth
Result output: act. ET, runoff,
Nitrogen load, …
Climate data input: P, Tmin, Tavg, Tmax,
sunshine, windspeed, rel. humidity
27

28. JAMS Features
28

29. • Build, configure and run models
• Manage component respository
• JAMS Cloud client interfaces
• Generate model documentation
Model Builder (JUICE)
29

30. 30
Model Builder (JUICE)

31. • Flexible model data I/O
– Varying data sources
– Transparent data output
• Fast processing of large data sets
• Data visualization & analysis
– time series & geo data
Model Data Exploration (JADE)
31
e.g.
x: time
y: space
z: attributes

32. 32
Model Data Exploration (JADE)

33. 33
Model Data Exploration (JADE)

34. Uncertainty analysis
Objective:
• Estimate uncertainty
of model simulation
• Apportion output
uncertainty to input
factor uncertainty
• Validate model
• Model Calibration
• Sensitivity/uncertainty analysis
Optimization Assistant (OPTAS)
Sensitivity analysis
Objective:
• Identify influencial
and non-influencial
input-factors
• Quantify parameter
interactions
• Analyse temporal
patterns of sensitivity
Step
1
Step
2
Objective:
• Fit model simulation to
observations
Challenges:
• Ill-posed multi-
objective
parameter optimization
problem
• Determine realisitic
values automatically
Optimization
Step
3
Calibrated model
Uncalibrated model
• Sensitivity analysis
• Optimization
• Solution evaluation
• Uncertainty analysis
Model
validation
34

35. JAMS Cloud Server Environment
REST interface:
• Exchange of libraries, input and
result data between clients and
server
• Job control / information
• User permission management
Advantages:
• Remote model simulation
• High-performance processing
• Load balancing
• Easy deployment from JAMS interface
35

36. 36
JAMS Cloud Server Environment

37. Parallel calibration
Parallel Processing (1)
Calibration Context
Calibration Control
• Sample generation
• Performance
evaluation
Parallel
Processing
37

38. Parallel simulation
Parallel Processing (2)
Temporal Context
Time
Concurrent Context
…
Spatial Partitioning
…
Spatial Context
SC 1 SC n
Spatial Context
Space
Parallel Processing
38

39. JAMS Documentation Generator
JAMS model
builder
PDF model
documentation
39

40. • Download available at JAMS website (http://jams.uni-
jena.de) for various platforms (JAVA based)
• Online documentation, tutorials and papers available
cover
– JAMS installation and configuration
– Tutorials covering creation of JAMS models
– Articles on setting up a development environment for
components
– Tutorials on JAMS tools
Download & Documentation
40

41. Acknowledgements
• Manfred Fink
• Peter Krause
• Christian Fischer

42. 42
Kralisch, S. & Fischer, C. (2012). Model representation, parameter calibration and parallel computing –
the JAMS approach. Proceedings of the International Congress on Environmental Modelling and
Software, Sixth Biennial Meeting (R. Seppelt, A. A. Voinov, S. Lange & D. Bankamp, eds.). Leipzig,
Germany.
Kralisch, S. & Krause, P. (2006). JAMS - A Framework for Natural Resource Model Development and
Application. Proceedings of the iEMSs Third Biannual Meeting (A. Voinov, A. Jakeman & A. E. Rizzoli,
eds.). Burlington, USA.
References