seri-index 1.0.2

Spatial Effective Rainfall Index — an event-scale ecological-effectiveness metric for hyper-arid environments

SERI — Spatial Effective Rainfall Index

Reference Python implementation of the Spatial Effective Rainfall Index (SERI) — an event-scale ecological-effectiveness metric for hyper-arid environments introduced in Selkh (2026).

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How to cite

If you use SERI in your research, please cite both the software and the concept paper:

Software

Selkh, C. (2026). SERI — Spatial Effective Rainfall Index
[Computer software]. Zenodo.
https://doi.org/10.5281/zenodo.20000268

Concept paper

Selkh, C. (2026). A Century After De Martonne: Why Spatial Coherence
is the Missing Dimension of Aridity in the Hyper-Arid Sahara.
Earth-Science Reviews, in review.

A ready-to-paste BibTeX entry is provided in CITATION.cff and via the Cite this repository button at the top of this page.

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What is SERI?

Classical aridity and drought indices (De Martonne 1926; SPI; SPEI; UNEP-AI) treat precipitation as a scalar quantity. In hyper-arid environments, two events of identical station-measured intensity can produce radically different ecological outcomes depending on whether the rainfall is confined to a small convective cell or distributed contiguously over a frontal-system footprint of several thousand square kilometres.

SERI elevates the contiguous spatial extent of a rainfall event to a first-class variable in the quantification of its ecological efficacy:

SERI = P · A^α · f(season) · g(substrate)

Symbol	Meaning	Unit
P	Event mean intensity over the footprint	mm
A	Contiguous area receiving rainfall ≥ 5 mm/day	km²
α	Sub-linear exponent on the spatial term (default 0.68 ¹)	–
f	Seasonal coefficient (winter 1.30, shoulder 0.65, summer 0.35)	–
g	Substrate coefficient (hamada 0.55 → wadi-bottom 1.60)	–

¹ Working value pending formal calibration on the n ≈ 150 event archive 2013–2024; see Selkh, in prep.

The index is operationally classified into six tiers, from Ecologically inert (SERI < 100) to Regional recharge (SERI ≥ 15 000).

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Installation

pip install seri

That's all you need for the core API. Optional extras:

pip install "seri[plot]"        # add matplotlib for figures
pip install "seri[earthengine]" # add the Google Earth Engine wrapper
pip install "seri[all]"         # everything

Windows: double-click launchers

If you prefer not to open a terminal, the scripts/ directory contains five .bat files, double-clickable from File Explorer:

File	Purpose
install.bat	First-time setup. Installs SERI + tests it.
run-gui.bat	Opens the SERI calculator window (Tkinter).
run-demo.bat	Runs the bundled Abadla 2015 demonstration.
run-notebook.bat	Launches Jupyter on the demo notebook.
run-tests.bat	Re-runs the 73-test suite.

The first time, run install.bat once; afterwards you can use any of the others directly. See scripts/README.md for details.

Command line

After installation, the seri command is available on PATH:

seri compute --P 10.79 --A 1624 --month 2 --substrate mixed
seri compute --P 10.79 --A 1624 --month 2 --substrate mixed --json
seri demo
seri tiers
seri gui          # open the graphical interface
seri info         # version and citation info

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Quick start

Reproduce the Abadla 2015 anchor case from the manuscript (§ 5.1):

import seri

result = seri.compute(
    P=10.79,           # mean intensity (mm)
    A=1624,            # contiguous area (km²)
    season=2,          # February → winter regime
    substrate="mixed", # area-weighted reg + wadi-bottom mix
)

print(result)
# SERIResult(value=2352.6, tier=PERENNIAL, P=10.79 mm, A=1624 km², α=0.68, f=1.3, g=1.1)

print(result.tier_name)
# 'Perennial response'

print(result.tier_description)
# 'Leaf-flush of established perennials; sustained NDVI anomaly 30-90 days.'

A batch version is available for archives:

events = [
    {"P": 10.79, "A": 1624, "season":  2, "substrate": "mixed"},
    {"P": 18.0,  "A":   51, "season":  7, "substrate": "reg"},   # convective cell
    {"P": 25.0,  "A": 8000, "season": 11, "substrate": "wadi_bottom"},
]
for r in seri.compute_batch(events):
    print(f"  SERI = {r.value:>7.0f}   →   {r.tier_name}")

For a complete walk-through with figures, see examples/notebook_demo.ipynb.

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The six ecological tiers

SERI value	Tier	Typical biological response
< 100	Ecologically inert	No measurable response; event dissipates by evaporation.
100 – 500	Microbial	Ephemeral biological soil-crust activation.
500 – 2 000	Annual germination	Therophytes and short-lived ephemerals.
2 000 – 5 000	Perennial response	Leaf-flush of established perennials; NDVI anomaly 30–90 d.
5 000 – 15 000	Wadi activation	Ephemeral flow, shallow-aquifer recharge.
≥ 15 000	Regional recharge	Exceptional event; deep-aquifer recharge.

These thresholds are calibrated for the Algerian Sahara transect El Bayadh – Béchar/Abadla – Timimoun-North. Transfer to other hyper-arid systems (Atacama, Namib, Karakum, Rub' al-Khali) requires regional re-calibration of the coefficients (see manuscript § 6.2).

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Optional Earth Engine wrapper

If you have earthengine-api installed and authenticated, you can run the full pipeline end-to-end from a Python session:

import ee
import seri.earth_engine as see

ee.Initialize()

aoi = ee.Geometry.Rectangle([-3.0715, 30.9064, -2.4085, 31.1336])  # Abadla AOI

result = see.compute_from_earth_engine(
    aoi=aoi,
    start_date="2015-02-22",
    end_date="2015-03-02",
    substrate="mixed",
)
print(result)

⚠️ The current GPM IMERG V07 release is known to under-detect Saharan rainfall events (Sun et al. 2018; Dezfuli 2017). For the Abadla anchor case specifically, the manuscript anchors the diachronic analysis on a deterministic 1 596 km² AOI rather than per-call IMERG re-detection (manuscript § 5.6).

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Roadmap

Version	Scope	Status
v1.0 (this release)	Public reference implementation of SERI-1 (concept paper)	✅
v1.1	Event-archive helpers, ROC plotting against SPI/SPEI	planned
v2.0 (SERI-2)	Continuous f(PET) = exp(−PET / PET_ref) from ERA5-Land	planned
v3.0 (SERI-S)	Satellite-only A from MSG/SEVIRI cloud-top temperature	planned
v4.0 (SERI-P)	Climate-projection extension on CMIP6 (SSP2-4.5 / SSP5-8.5)	planned

The empirical calibration of α with bootstrap confidence intervals on the 2013–2024 archive (n ≈ 150) is reported in the companion paper (Selkh, in preparation).

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Contributing

Pull requests are welcome for:

• Bug fixes in the reference implementation
• Documentation improvements
• New examples or notebooks

For substantive scientific changes (different default α, new tier boundaries, changes to the f or g coefficients), please open an issue first to discuss the rationale.

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License

Apache License 2.0 — see LICENSE.

This permissive licence allows commercial and non-commercial use, redistribution and modification, provided that proper attribution to the original author is preserved.

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Acknowledgements

This software accompanies a decade of field campaigns 2013–2024 across the Algerian Sahara transect supported by Université Ahmed Draia d'Adrar. NASA, JAXA and the Climate Hazards Center (UC Santa Barbara) are acknowledged for free public access to the GPM IMERG, MODIS MOD13A3 and CHIRPS products on which the pipeline depends.