This document describes the scientific methodology for assessing the spatial transportability of Vietnam's Alternative Wetting and Drying (AWD) agricultural water management policy to Japan.
Research Question: To what extent is AWD technically feasible in Japanese rice production systems?
The core analysis implements the MapAWD (Map-based Assessment of AWD) framework, which evaluates suitability based on dekad-level (10-day period) water availability.
Water Balance Equation (dekad-level):
Where:
-
$WB_t$ = Water balance in dekad$t$ (mm) -
$P_t$ = Rainfall in dekad$t$ (mm) [CHIRPS] -
$ET_t$ = Reference evapotranspiration in dekad$t$ (mm) [MODIS] -
$K_t$ = Percolation loss in dekad$t$ (mm) [SoilGrids-derived]
Seasonal Timing:
- Season spans dekads 10-28 (May 15 - September 25)
- Corresponds to main rice growing season in temperate Asia
- 19 dekads total per season
Exclusion Windows:
- Exclude first 2 dekads (10-11): Establishment phase, fields need water
- Exclude last 1 dekad (28): Harvest phase, drying not beneficial
- Analysis dekads: 12-27 (16 dekads for suitability assessment)
AWD feasibility requires that fields periodically dry without crop stress. A dekad is considered suitable if it meets both conditions:
-
Deficit Condition:
$WB_t < 0$ (field experiences water deficit) -
Threshold Condition:
$WB_t \geq T$ (deficit within safe range)
Where
Interpretation:
- More negative
$T$ = stricter criterion = fewer suitable dekads -
$T = -150$ mm: Only extreme deficits qualify (most restrictive) -
$T = -25$ mm: Mild deficits qualify (most lenient)
For each pixel and threshold:
Where:
-
$n_{suitable}$ = Count of dekads where$WB_t < 0$ AND$WB_t \geq T$ -
$n_{analysis}$ = Count of analysis dekads (typically 16) -
$f_{suit}$ = Fraction of suitable dekads (0-1)
Fraction converted to 3-class scale:
| Class | Name | Condition |
|---|---|---|
| 3 | High |
|
| 2 | Moderate |
|
| 1 | Low |
|
Rationale: High and moderate classes indicate sufficient dry periods for beneficial AWD. Low class indicates climate insufficiently supports AWD cycles.
Collection: UCSB-CHG/CHIRPS/DAILY
Resolution: 5 km
Temporal: Daily, 1981-present
Processing:
- Extract 10-day windows for each dekad
- Sum daily precipitation to dekad totals
- Units: mm/dekad
Quality Notes:
- Gold-standard rainfall dataset for agriculture (widely cited)
- Based on satellite + rain gauge fusion
- Suitable for regional hydrological studies
Collection: MODIS/061/MOD16A2
Resolution: 500 m
Temporal: 8-day composites, 2000-present
Processing:
- Extract 8-day ET0 values
- Reproject to study area grid
- Convert scale factor 0.1 to mm
-
Dekad interpolation: When dekad boundary crosses 8-day tile boundaries, use weighted average:
$$ET_{dekad} = \frac{n_1 \cdot ET_1 + n_2 \cdot ET_2}{10}$$ where$n_1, n_2$ are days in each tile - Units: mm/dekad
Quality Notes:
- MODIS ET widely validated against flux tower networks
- Accounts for seasonal vegetation dynamics
- Higher resolution (500m) captures local heterogeneity
Collections:
projects/soilgrids-isric/clay_mean(clay %, 0-5cm)projects/soilgrids-isric/sand_mean(sand %, 0-5cm)
Resolution: 250 m
Processing:
- Extract clay and sand percentages
- Calculate silt as: silt = 100 - clay - sand
- Classify into 4 drainage classes based on texture
Drainage Classification:
| Class | Clay (%) | Soil Type | Percolation (mm/day) |
|---|---|---|---|
| 1 (Well) | <20 | Sandy | 12.0 |
| 2 (Moderate) | 20-35 | Loam | 8.0 |
| 3 (Imperfect) | 35-50 | Clay loam | 4.0 |
| 4 (Poor) | ≥50 | Clay | 3.0 |
Percolation Rates: Based on USDA Soil Survey Manual estimates for rice-growing regions.
Collection: USGS/SRTMGL1_Ellip
Resolution: 30 m
Processing:
- Compute slope using Sobel filter
- Classify slopes: feasible if
$< 10°$ - Rationale: Steep slopes complicate water management
User-provided asset (custom classification)
Content: Binary raster where 1 = rice paddy, 0 = other
Sources (alternatives):
- User's own field surveys / high-resolution imagery
- MODIS Land Use Classification (annual)
- ESA WorldCover (global, 10m resolution)
Beyond water balance, real-world AWD implementation requires favorable terrain and soil conditions.
A pixel is deemed biophysically feasible only if:
Where:
- Slope OK: Slope < 10° (flat terrain for uniform water management)
- Drainage OK: Drainage class ≤ 3 (excludes poorly-drained clay soils)
- WB Moderate or High: Suitability class ≥ 2 (minimum water balance support)
Quantifies which constraints most limit feasibility:
- What % of area fails slope criterion?
- What % fails drainage criterion?
- What % fails water balance criterion?
- How many fail all three?
Identifies whether extension programs should focus on:
- Training (water management knowledge)
- Structural change (terracing for steep areas)
- Subsidy reform (encourage in favorable zones)
Spatial clustering of suitable areas affects extension feasibility. Highly fragmented suitable zones require higher per-unit-area extension costs.
Fragmentation Metric:
Where:
-
$\bar{A}$ = Mean patch size (hectares) -
$A_{max}$ = Maximum patch size (hectares) -
$F \in [0, 1]$ , higher = more fragmented
Interpretation:
-
$F = 0.01$ : One large contiguous zone (no fragmentation) -
$F = 0.5$ : Patches at half max size (moderate fragmentation) -
$F = 1.0$ : All patches equal size (maximal fragmentation)
Aggregates suitability statistics by administrative regions:
For each region
Identifies priority regions for Phase 1 implementation (highest suitability + lowest fragmentation).
Compares the 3.8× fragmentation ratio to quantify transportability costs:
- Vietnam (F ≈ 0.1): Multiplier ≈ 1.4×
- Japan (F ≈ 0.25): Multiplier ≈ 2.0×
Higher multiplier in Japan indicates extension requires more resources per square kilometer.
Since deficit threshold (
Output: DataFrame with columns:
| threshold_mm | fraction_suitable | suitability_class | percentage_suitable |
|---|---|---|---|
| -25 | 0.75 | 3 (High) | 75% |
| -50 | 0.60 | 2 (Moderate) | 60% |
| -70 | 0.40 | 2 (Moderate) | 40% |
| ... | ... | ... | ... |
| -150 | 0.05 | 1 (Low) | 5% |
Interpretation:
- If suitability "robust" across thresholds → confident recommendation
- If suitability "cliff" (drops sharply) → sensitive to threshold choice
| Source | Uncertainty | Impact |
|---|---|---|
| CHIRPS Rainfall | ±10-15% regionally | Threshold sweep captures this |
| MODIS ET | ±10% over vegetables | Acceptable for relative comparison |
| SoilGrids | ±5-10% clay/sand | Drainage classes robust to error |
| SRTM DEM | ±10m elevation | Slope classification robust |
| Rice Map | ±5-10% area | Boundary fuzziness minor |
- Reference ET approximates paddy ET: MODIS ET0 developed for general vegetation; rice-specific adjustments not applied
- Dekad aggregation sufficient: Sub-dekad water stress dynamics not captured
- No irrigation simulation: Current modeling assumes rain-fed only
- Soil texture static: Assumes 2020 soil map applies to analysis year
- Climate stationarity: Historical climate patterns repeat in analysis year
- Groundwater contribution to water balance
- Farmer risk tolerance and labor availability
- Market prices and profitability analysis
- Pest/disease dynamics with increased water stress
- Climate change impacts on future suitability
- Qualitative: Compare results to agronomy literature on AWD feasibility
- Quantitative: Sensitivity analysis (vary thresholds, exclusion windows)
- Spatial: Inspect maps for unreasonable artifacts or clustered errors
- Temporal: Compare multiple years to assess consistency
- All parameters in
config/config.yaml - All functions vectorized with input validation
- Logging at each pipeline stage
- Type hints for interface clarity
- Unit tests in
/tests/(future implementation)
AWD Literature:
- Lampayan, R.M., et al. (2015). "Adoption and economics of alternate wetting and drying in irrigated rice in Southeast Asia." Field Crops Research, 170, 95-108.
- Bouman, B.A.M., et al. (2007). "Rice and water." Advances in Agronomy, 92, 187-237.
Remote Sensing Data:
- Funk, C., et al. (2015). "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes." Scientific Data, 2, 150066.
- Running, S.W., et al. (2017). "A continuous satellite-derived measure of global terrestrial primary production." BioScience, 54(7), 547-560.
Geospatial Methods:
- Hijmans, R.J. (2012). "Cross-validation of species distribution models: removing spatial sorting bias." Ecology, 93(3), 679-688.
| Term | Definition |
|---|---|
| AWD | Alternate Wetting and Drying: water management practice alternating flooding and drying |
| Dekad | 10-day period; 36 dekads = 360 days per year |
| ET0 | Reference evapotranspiration (standardized to grass/alfalfa) |
| Percolation | Water loss through soil downward movement (mm/day) |
| Fragmentation | Spatial dispersion of suitable patches; higher = more scattered |
| Suitability | Binary classification: suitable or unsuitable for AWD |
| Threshold | Critical water deficit level (negative mm) defining suitability boundary |
Document Version: 1.0
Last Updated: January 2026
Corresponding Code: src/water_balance/__init__.py, src/spatial_analysis/__init__.py