Turning the D-USYS model landscape into a shared resource—and a perspective paper
The model inventory becomes the evidence base for a perspective paper—and the paper gives the inventory a reason to exist and remain useful.
We need to agree on the scientific question before designing the database.
“Which models do D-USYS groups use?”
“What does our diversity reveal about the future of environmental modelling?”
Environmental science contains radically different modelling traditions. D-USYS offers a bounded but unusually diverse test bed for finding shared pressures, bottlenecks and emerging directions.
forest · crop · soil · hydrology · atmosphere · biodiversity · economics · remote sensing · coupled human–environment systems
The paper’s relevance comes from the general argument; local examples make that argument concrete.
| Dimension | Examples |
|---|---|
| Model / framework | name, link, group |
| Domain | forest, crop, climate, hydrology, biodiversity, soil, economics… |
| Purpose | understanding, prediction, scenarios, decision support, teaching |
| Spatial scale | organism → plot → landscape → regional → global |
| Temporal scale | hours → centuries |
| Approach | process-based, statistical, ML/AI, hybrid, optimization, agent-based… |
| Representation | deterministic/stochastic, individual/aggregate, spatial/non-spatial |
One record should take approximately 10–15 minutes—not become a model documentation project.
| Dimension | Examples |
|---|---|
| Inputs | experiments, monitoring, remote sensing, inventories, reanalysis… |
| Outputs | none / external scenario / management / behaviour / coupled |
| Human dimension | understanding, prediction, scenarios, decision support, teaching |
| Uncertainty | whether/how represented |
| Development status | used / adapted / actively developed |
| Interoperability | standalone / coupled / modular |
| Main current limitation | one short free-text answer |
| Future need | “What would make this model substantially more useful in 10 years?” |
One record should take approximately 10–15 minutes—not become a model documentation project.
Scale
Complexity
Data
Uncertainty
Coupling
Decisions
Software + AI
The intellectual contribution is not that disciplines use different models. It is that different models may be confronting the same structural pressures.
| Phase | Collective task | Output |
|---|---|---|
| 1. Frame | Agree on 6–8 propositions and paper scope | Concept note + provisional author group |
| 2. Pilot | Develop ad test the questionnaire with 3 contrasting groups | Revised schema + feasibility check |
| 3. Collect | One contact per group; structured response + short reflection | Searchable inventory |
| 4. Interrogate | Descriptive patterns + thematic synthesis + challenge workshop | Figures and argument map |
| 5. Write | Small writing team; contributors verify interpretation | Perspective manuscript |
| 6. Sustain | Assign ownership, update cycle and public/private layers | Useful living resource |
Start with the scientific argument. Collect only what the argument—and a genuinely useful resource—requires.