Watershed Land Cover Simulation — Markov-CA

Predicted land cover 2030 — Simulated 2030 land cover — urban growth concentrates near the existing core and major highways

01The problem

Urban growth reshapes watershed hydrology — more impervious surface means more runoff, more flood risk, and less groundwater recharge. Knowing where a city will expand lets planners act before the change happens.

This isn't a snapshot — it's a prediction, built from real transitions and validated against what actually happened.

Delineated watershed study area — Watershed of San Antonio River near Falls City (~5,453 km²)

02Method

Delineate watershed from DEM (Fill → Flow Direction → Flow Accumulation → Snap → Watershed).
Reclassify NLCD 2010 & 2020 into 5 classes.
Compute the 2010→2020 Markov transition matrix in Python.
Build CA spatial factors: distance to roads, highways, facilities, plus 3×3 neighborhood influence.
Calibrate weights & threshold against observed 2020, then simulate 2030.

Calibration workflow — Iterative calibration loop — maximize Figure of Merit

03Transition probabilities

The Markov matrix quantifies how likely each land cover was to change between 2010 and 2020. Built land was highly stable (>99% stays built), while barren, agriculture, and forest fed the growth.

Neighborhood influence (β4 = 7) was the dominant driver — new growth clusters next to existing development.

From \ To	Built	Barren	Forest	Agri
Built	0.994	0.004	0.001	0.001
Barren	0.041	0.901	0.045	0.012
Forest	0.022	0.045	0.932	0.001
Agriculture	0.032	0.087	0.000	0.880

Neighborhood influence layer — Neighborhood influence — the strongest driver

04Validation & forecast

0.85Figure of Merit (validation)

7.6% / 6.6%False negatives / false positives

~5,453 km²Watershed study area

Validation compared the model's predicted 2020 against observed 2020 — a strong match confirmed the model before it was used to forecast 2030. The 2030 simulation shows continued growth near the city core and along highways.

2020 validation — Predicted vs. observed 2020 (validation)

New growth forecast 2030 — Where new growth is forecast by 2030

Built-up area trend 2010-2030 — Built-up area share rising across 2010 → 2020 → 2030

05Code

The model is driven by two Python scripts, both on GitHub:

markov_transition_matrix.py — reads the 2010 and 2020 land-cover rasters and computes the Markov transition count and probability matrices with NumPy and rasterio.
calibration_figure_of_merit.py — compares the simulated 2020 built-up map against observed 2020 to compute the Figure of Merit, false positives, and false negatives (arcpy + NumPy).

06Limitations & next steps

Drivers limited to proximity and neighborhood (next: demographics, economics, policy, climate).
Two-date Markov assumes stationary transitions; 30 m resolution; single watershed.

Read the code

Python scripts (Markov matrix + calibration), README, and all maps on GitHub.

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