Environmental modelling under pressure

Turning the D-USYS model landscape into a shared resource—and a perspective paper

Modelers @ USYS

One initiative, two useful outputs

The model inventory becomes the evidence base for a perspective paper—and the paper gives the inventory a reason to exist and remain useful.

A catalogue alone will not sustain engagement

The weak version

“Which models do D-USYS groups use?”

  • Quickly becomes outdated
  • Produces descriptive counts
  • Has limited external relevance

The stronger version

“What does our diversity reveal about the future of environmental modelling?”

  • Starts from testable propositions
  • Collects only analytically useful fields
  • Makes cross-disciplinary experience visible
  • Builds an argument beyond D-USYS

D-USYS is the microcosm—not the subject

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.

A deliberately lightweight model landscape (1/2)

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

A deliberately lightweight model landscape (2/2)

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?”

The data collection should expose shared problems across disciplines

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.

An idea for paper propositions / paper scope / structure

  1. Environmental models are being asked to do more
    The pressures: global change, extremes, decisions, data and AI.
  2. A landscape of modelling approaches
    Briefly introduce the D-USYS evidence base.
  3. Different models, surprisingly similar problems
    Identify recurring cross-disciplinary bottlenecks.
  4. Where modelling is heading
    Evaluate the transitions already underway.
  5. What should change
    Make an opinionated case about priorities and infrastructure.
  6. A research agenda for the next decade
    End with five concrete commitments—not “more research is needed.”

A practical route from mixer idea to publishable work

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

Discussions points for today

  1. Is the combined inventory + perspective paper concept worth pursuing?
  2. Which one central claim is sufficiently important—and contestable?
  3. Which 6–8 propositions should shape the questionnaire?
  4. Who will join a small design group for the pilot?

Start with the scientific argument. Collect only what the argument—and a genuinely useful resource—requires.