The Groundwater Potential Index (GWPI) is grounded in the hydrogeological Source–Pathway–Receptor conceptual model, adapted for remote sensing in data-scarce crystalline basement terrains common across sub-Saharan Africa. Three independently normalised pillars (0–1) are combined using user-adjustable weights, then multiplied by a terrain constraint. The final score ranges from 0 to 10.
The Receptor — how much saturated weathered rock exists below the water table
The Source — is there enough climatic moisture surplus to recharge the aquifer
The Pathway — can the aquifer transmit water to a borehole via fractures and permeable soils
Multiplicative penalty — steep/elevated terrain collapses the score to zero regardless of pillar values
In crystalline basement aquifers, groundwater is primarily hosted in the weathered regolith (saprolite). The thickness of this saturated weathered zone is the primary control on how much water the aquifer can store. We estimate this using two globally available datasets:
A hilltop with 50 m of DTB may still be dry if it sits 60 m above the nearest stream (HAND = 60). Subtracting HAND from DTB estimates how much of the weathered zone is likely saturated — i.e., below the drainage base-level that approximates the water table.
The raw difference has a long tail — some pixels show >150 m of potential saturated thickness. A linear scale would compress all the action into the low end. The log(1+x) transform compresses the tail while preserving better discrimination in the critical 0–50 m range where most boreholes operate. Dividing by log(201) ensures that 200 m maps to exactly 1.0.
Groundwater recharge requires a moisture surplus after evapotranspiration is satisfied. We use two complementary proxies:
A wet climate (high AI) still won't deliver much recharge to an isolated ridge with tiny upstream area. Conversely, a huge catchment in a hyper-arid zone won't help either. The product ensures both conditions must be met — if either is near zero, Supply collapses.
Even with adequate storage and recharge, the aquifer must be able to transmit water to a borehole. In crystalline basement terrains, this is controlled by fracture networks, soil permeability, and weathering intensity. We blend five sub-components:
| Component | Weight | Source | Rationale |
|---|---|---|---|
| FracturesN | 28% | Canny edge detection on MERIT elevation + 150 m Gaussian blur | Heuristic proxy for lineaments and fracture damage zones (~100 m wide) |
| SoilTrans | 28% | SoilGrids v2: sand%, clay%, CFVO%, bulk density (depth-weighted 0–60 cm) | Soil hydraulic transmissivity proxy — sandy, coarse soils allow more infiltration |
| TPIN | 16% | elv − focal_mean(elv, 250 m circle) | Topographic Position Index — valleys and convergence zones collect water |
| NDVIN | 16% | Dry-season Sentinel-2 NDVI median (10 m, aggregated) | Phreatophytes as biological sensors — trees that stay green in dry season are accessing groundwater |
| LithFactor | 12% | Neutral 0.5 constant (or GFV lithology if enabled) | Lithological prior — does not drive results unless explicitly enabled |
The Canny edge detector identifies sharp topographic breaks in the MERIT elevation surface that often correspond to fault traces and fracture zones. The result is convolved with a 150 m Gaussian kernel to simulate the ~100 m damage zone around each lineament where fracture permeability is enhanced. This is normalised to 0–1 using the range 0–0.30.
Uses a depth-weighted average (0–60 cm) of four SoilGrids v2 properties to estimate soil hydraulic transmissivity:
In the late dry season, most vegetation has senesced. Trees and shrubs that remain green are likely phreatophytes — species with root systems tapping shallow groundwater. Sentinel-2 NDVI is computed at 10 m resolution from a cloud-masked median composite, then aggregated and normalised (0.05–0.60 → 0–1). This is one of the most powerful signals in data-scarce settings.
Many groundwater potential maps use purely additive models. This creates a dangerous failure mode: mountainous terrain scores highly because fracture density and slope features add positive signal. But in steep rocky terrain, gravity moves water as surface runoff before it can infiltrate.
The solution is a multiplicative penalty that can drive the score to zero regardless of pillar values:
| Terrain Type | Slope | HAND | Penalty | Effect |
|---|---|---|---|---|
| Flat valley floor | 2° | 3 m | ≈ 0.95 | Pillars dominate |
| Gentle hillslope | 8° | 15 m | ≈ 0.45 | Moderate suppression |
| Steep hillslope | 15° | 40 m | ≈ 0.07 | Strong suppression |
| Mountain ridge | 25° | 80 m | ≈ 0.001 | Score ≈ zero |
The factor of 10 maps the 0–1 weighted sum into a more intuitive 0–10 range. The multiplicative penalty ensures that no amount of positive pillar signal can overcome genuinely unfavourable terrain.
| Score | Class | Guidance |
|---|---|---|
| 0 – 2.0 | Very Low | Low combined evidence. De-prioritise unless strong local knowledge exists. |
| 2.0 – 3.5 | Low | Some signal, but weak. Cross-reference with borehole logs before acting. |
| 3.5 – 5.0 | Medium | Promising. Identify which pillar(s) are driving the score and verify locally. |
| 5.0 – 6.2 | High | Strong screening signal. Good candidate for geophysical survey + test borehole. |
| 6.2 – 10 | Exceptional | Top-tier signal. Still verify geology, siting constraints, and water quality. |