Executive brief

Syria 2026 agricultural shocks — first-half-2026 snapshot

The bottom line

The 2026 floods and crop fires reversed only a fraction of Syria’s tentative 2026 cereal recovery — real and concentrated, not a second collapse.

~25,925 t cereal-production loss (lower bound; honest range 3,471–39,946 t).
2.16% of the 2025 drought floor; concentrated on the Euphrates (Raqqa and Deir ez-Zor each ~7–8% of their own baseline).
~105,504 ha flooded + ~10,533 ha burned cropland.

The spine. Syria’s 2025 baseline was a record drought (cereal harvest near 1.2 Mt, worst in ~60 years). 2026 brought a tentative recovery (projected ~2.3 Mt) — then a shock: Euphrates flooding (an anomaly on a desertification trajectory) plus a national crop-fire signal. This study measures how much of the recovery the shock took back.

Choropleth map of Syria shaded by 2026 cropland-production loss as a percent of the 2025 baseline; Raqqa and Deir ez-Zor darkest at about 7.6 to 7.8 percent, Hasakah 2.8 percent, all others pale, Latakia and Damascus City grey. — 2026 cropland-production loss as a share of each governorate’s 2025 baseline. First-half-2026 lower bound.

The three caveats that matter most

These are lower bounds. The snapshot covers floods Mar–Jun and fires May 1 – Jun 12 2026; the summer harvest and fire peak is unobserved. Re-run post-harvest for the concluded result (DEC-039).
One flood figure is flagged. Hasakah’s June flood hectares have no water source and are a likely radar harvest artifact (RQ1). Excluding them moves the national total only ~5%, so the headline is robust.
Not an IPC classification. This is a production-shock signal that feeds a GIEWS / FEWS NET / IPC assessment, layered on an already record-drought baseline. It is not an IPC phase.

What the attribution found

Floods are upstream-sourced Euphrates water (zero local rainfall, ~6× the drought-season discharge). Natural-versus-managed release is not resolvable from the data (RQ1).
Fires are an agricultural drought and heat hazard, not frontline-linked; the 2026 conflict overlay cannot be computed (ACLED data gap) and there is no active armed conflict in the window (RQ2).
Geography of damage is mapped descriptively against indicative control areas, with no comparative claim (RQ3).

For the full method, evidence, and per-governorate detail, see the Overview and Food security pages.

Syria 2026 Agricultural Shocks · first-half-2026 snapshot (floods Mar–Jun; fires May 1 – Jun 12 2026) · figures are lower bounds (DEC-039). Author: Muhanad Abulhusn. Rendered from human-validated (Tier-2) records only.

Data sources: FAO/GIEWS (baseline & production), GloFAS (discharge), CHIRPS (rainfall), VIIRS/FIRMS (active fire), Sentinel-1/2 & Copernicus EMS (flood/burn extent), ACLED (conflict, cited not republished). License: CC BY 4.0.

Suggested citation:

Abulhusn, M. (2026). Syria 2026 Agricultural Shocks: floods and crop fires against the 2025 record-drought baseline (first-half-2026 snapshot). https://muhanad-husn.github.io/flood-fire/

Provenance & contracts: PRODUCT.md · STRUCTURE.md · decision log · source repo

--- title: "Executive brief" subtitle: "Syria 2026 agricultural shocks — first-half-2026 snapshot" toc: false --- ```{python} #| label: setup #| echo: false import sys, pathlib import pandas as pd REPO_ROOT = None for _cand in (pathlib.Path.cwd(), *pathlib.Path.cwd().parents): if (_cand / "CLAUDE.md").exists() and (_cand / "viz").is_dir(): REPO_ROOT = _cand sys.path.insert(0, str(_cand)) break assert REPO_ROOT is not None, "could not locate repo root (CLAUDE.md + viz/)" OUT = REPO_ROOT / "outputs" nat = pd.read_csv(OUT / "food_security" / "impact_national.csv") impact = pd.read_csv(OUT / "food_security" / "impact_by_aoi.csv") tot = nat.loc[nat["aoi_id"] == "STUDY_TOTAL"].iloc[0] flood_ha = impact.loc[impact.phenomenon == "flood", "damaged_ha_headline_union"].sum() fire_ha = impact.loc[impact.phenomenon == "fire", "damaged_ha_headline_union"].sum() ``` <a class="print-button" href="javascript:window.print()">🖨 Print / Save as PDF</a> ::: {.key-findings} ### The bottom line [The 2026 floods and crop fires reversed only a **fraction** of Syria's tentative 2026 cereal recovery — real and concentrated, not a second collapse.]{.headline-number style="font-size:1.15rem;"} ```{python} #| label: brief-numbers #| echo: false from IPython.display import Markdown Markdown(f""" - **~{tot.production_loss_t_headline:,.0f} t** cereal-production loss (lower bound; honest range **{tot.production_loss_t_low:,.0f}–{tot.production_loss_t_season_ref:,.0f} t**). - **{tot.loss_pct_of_baseline_headline:.2f}%** of the 2025 drought floor; concentrated on the **Euphrates** (Raqqa and Deir ez-Zor each ~7–8% of their own baseline). - **~{flood_ha:,.0f} ha** flooded + **~{fire_ha:,.0f} ha** burned cropland. """) ``` ::: **The spine.** Syria's 2025 baseline was a **record drought** (cereal harvest near 1.2 Mt, worst in ~60 years). 2026 brought a **tentative recovery** (projected ~2.3 Mt) — then a **shock**: Euphrates flooding (an anomaly on a desertification trajectory) plus a national crop-fire signal. This study measures how much of the recovery the shock took back. ```{python} #| label: brief-map #| echo: false #| fig-format: png #| fig-dpi: 200 #| fig-cap: "2026 cropland-production loss as a share of each governorate's 2025 baseline. First-half-2026 lower bound." #| fig-alt: "Choropleth map of Syria shaded by 2026 cropland-production loss as a percent of the 2025 baseline; Raqqa and Deir ez-Zor darkest at about 7.6 to 7.8 percent, Hasakah 2.8 percent, all others pale, Latakia and Damascus City grey." from viz import choropleth, caveat_footer, CAVEATS import matplotlib.pyplot as plt govs = nat[nat.aoi_id != "STUDY_TOTAL"] vals = dict(zip(govs.aoi_id, govs.loss_pct_of_baseline_headline)) fig = choropleth(vals, title="2026 cropland-production loss (% of 2025 baseline)", cbar_label="loss, % of 2025 baseline", value_fmt="{:.1f}%") caveat_footer(fig, CAVEATS["case_study_2026h1"]) plt.show() ``` ### The three caveats that matter most 1. **These are lower bounds.** The snapshot covers floods Mar–Jun and fires May 1 – Jun 12 2026; the summer harvest and fire peak is unobserved. Re-run post-harvest for the concluded result ([DEC-039](glossary.qmd#dec-039)). 2. **One flood figure is flagged.** Hasakah's June flood hectares have no water source and are a likely radar harvest artifact ([RQ1](rq1.qmd)). Excluding them moves the national total only ~5%, so the headline is robust. 3. **Not an IPC classification.** This is a production-shock signal that *feeds* a GIEWS / FEWS NET / IPC assessment, layered on an already record-drought baseline. It is not an IPC phase. ### What the attribution found - **Floods** are **upstream-sourced** Euphrates water (zero local rainfall, ~6× the drought-season discharge). Natural-versus-managed release is **not resolvable** from the data ([RQ1](rq1.qmd)). - **Fires** are an **agricultural drought and heat hazard**, not frontline-linked; the 2026 conflict overlay cannot be computed (ACLED data gap) and there is no active armed conflict in the window ([RQ2](rq2.qmd)). - **Geography** of damage is mapped descriptively against indicative control areas, with **no comparative claim** ([RQ3](rq3.qmd)). For the full method, evidence, and per-governorate detail, see the [Overview](index.qmd) and [Food security](food_security.qmd) pages. {{< include _footer.qmd >}}