mohitanand/forest_mortality

200,000 Years of Weather and Quasi-Stationary Tree Dynamics Simulation for Beech, Pine, and Spruce Forests

A large synthetic benchmark coupling a stochastic weather generator (AWE-GEN) with a process-based forest gap model (FORMIND) to study how weather time series drive forest biomass mortality. It is designed as a test bed for machine-learning models that map weather (and forest structure) time series to an ecological impact — a genuinely multi-modal, time-series → regression setting.

Species covered: European beech, Scots pine, and Norway spruce.

This is a richer, higher-resolution version of an earlier monthly-averaged simulation: the weather and forest dynamics here are provided at daily resolution (raw) and as a 5-day / pentad aggregate (processed), rather than monthly.

Overview

Weather is generated with the hourly stochastic weather generator AWE-GEN; its aggregated daily output (precipitation, temperature, radiation) drives the individual-based forest gap model FORMIND. The dataset provides annual forest biomass mortality rates together with per-year histograms of five structure variables — age, stem volume, leaf area index (LAI), height, and diameter. All data is stored as HDF5 files.

Temporal resolution. raw_simulation/ and processed/ are the same simulation at two aggregation levels — the raw FORMIND output is daily, and the processed splits are its 5-day (pentad) aggregate, prepared for model training.

Repository structure

forest_mortality/
├── raw_simulation/          # FORMIND output, DAILY resolution (20-member ensemble / species)
│   ├── beech/               #   beech_dynMort_0.h5 … beech_dynMort_19.h5
│   ├── pine/                #   pine_dynMort_0.h5  … pine_dynMort_19.h5
│   └── spruce/              #   spruce_dynMort_0.h5 … spruce_dynMort_19.h5
│
└── processed/               # ML-ready PENTAD (5-day) aggregate, split (beech & pine)
    ├── train_MBR_beech_pentad_3_years_10000ha.h5
    ├── val_MBR_beech_pentad_3_years_10000ha.h5
    ├── test_MBR_beech_pentad_3_years_10000ha.h5
    ├── train_MBR_pine_pentad_3_years_10000ha.h5
    ├── val_MBR_pine_pentad_3_years_10000ha.h5
    ├── test_MBR_pine_pentad_3_years_10000ha.h5
    ├── bins_Xs_train_beech.npy    # structure-variable histogram bin edges
    └── bins_Xs_train_pine.npy

raw_simulation/

Raw FORMIND simulation output at daily resolution, organised per species. Each species folder holds a 20-member ensemble (*_dynMort_0.h5 … *_dynMort_19.h5) of dynamic-mortality runs on a simulated 10,000 ha stand — the full simulated forest dynamics (per-year structure histograms and mortality) before any train/val/test partitioning.

processed/

Model-ready pentad (5-day aggregate) splits for beech and pine, named {split}_MBR_{species}_pentad_{n_years}_years_10000ha.h5:

• MBR — the prediction target: (mortality) biomass rate.
• pentad — weather aggregated to 5-day steps.
• 3_years — length of the input weather window per sample.
• 10000ha — simulated stand area.

Each HDF5 file provides the arrays consumed by the training pipeline:

Array	Meaning
Xd	dynamic weather inputs (pentad precipitation, temperature, radiation time series)
Xs	static / structural forest-state features
Y	target biomass mortality rate

The bins_Xs_train_*.npy files hold the histogram bin edges for the structure variables (age, stem volume, LAI, height, diameter), used to reproduce / interpret the Xs histograms.

Intended uses

• Benchmarking sequence models (Transformers, RNNs, TCNs, etc.) on long weather time series with an ecological regression target.
• Studying multi-modal learning: combining dynamic weather with static forest structure.
• Investigating how weather variability propagates to forest mortality under quasi-stationary conditions.

Quick start

import h5py

# processed pentad split
with h5py.File("processed/train_MBR_beech_pentad_3_years_10000ha.h5", "r") as f:
    print(list(f.keys()))          # inspect available arrays
    # Xd, Xs, Y = f["Xd"][:], f["Xs"][:], f["Y"][:]

# one raw (daily) ensemble member
with h5py.File("raw_simulation/spruce/spruce_dynMort_0.h5", "r") as f:
    print(list(f.keys()))

# download the whole dataset locally
from huggingface_hub import snapshot_download
snapshot_download("mohitanand/forest_mortality", repo_type="dataset")

Models & tools

• AWE-GEN — hourly stochastic Advanced WEather GENerator.
• FORMIND — process-based, individual-based forest gap model.

Authors

• Mohit Anand
• Jakob Zscheischler

License

Released under CC-BY-4.0.