TY  - JOUR
AU  - Rasul, Azad
PY  - 2026
DA  - 2026/09/24
TI  - EWLR - A New Method for Interpolating Elevation-Driven Variables: Annual Rainfall in Erbil Governorate
JO  - Journal of Geoscience and Earth Observation
T2  - Journal of Geoscience and Earth Observation
JF  - Journal of Geoscience and Earth Observation
VL  - 1
IS  - 2
SP  - 158
EP  - 171
DO  - 10.62762/JGEO.2026.171666
UR  - https://www.icck.org/article/abs/JGEO.2026.171666
KW  - rainfall interpolation
KW  - Elevation-Weighted Local Regression (EWLR)
KW  - orographic enhancement
KW  - Kurdistan Region
KW  - mountainous terrain
KW  - spatial precipitation mapping
KW  - hydrological modeling
AB  - Accurate spatial estimation of rainfall is critical for hydrological modeling, water resource management, and agricultural planning---particularly in mountainous and semi-arid regions with sparse monitoring networks. This study presents an Enhanced Elevation-Weighted Local Regression (EWLR) model to generate a high-resolution (30~m) annual rainfall surface for Erbil Governorate, northern Iraq. The EWLR model integrates distance weighting, elevation similarity weighting, and orographic enhancement within a locally weighted regression framework. Average annual rainfall, derived from rainy seasons spanning 1997--1998 to 2024--2025 across 19 meteorological stations, along with a 30~m resolution digital elevation model (DEM), were used to construct and validate the model. Hyperparameters were optimized via Leave-One-Out Cross-Validation (LOOCV), and performance was benchmarked against conventional methods including Inverse Distance Weighting (IDW), Kriging, Thin-Plate Spline, and Radial Basis Function interpolation. Results indicate that EWLR outperforms all benchmarks, achieving $R^2 = 0.797$, RMSE $= 120.9$~mm, and MAE $= 87.46$~mm. Rainfall shows a strong positive correlation with elevation ($r = 0.907$, $p < 0.001$), increasing nearly fivefold from lowland plains ($\sim$270~mm) to mountainous areas ($>$1{,}300~mm). The final high-resolution rainfall map captures orographic effects reasonably well, providing a physically consistent, statistically robust dataset suitable for hydrological, climatic, and environmental modeling in data-sparse mountainous regions. The methodology offers a reproducible, elevation-centric framework adaptable to other elevation-driven variables (e.g., temperature lapse rates or snow accumulation) and complex terrains with limited observations.
SN  - 3144-2692
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Rasul2026EWLR,
  author = {Azad Rasul},
  title = {EWLR - A New Method for Interpolating Elevation-Driven Variables: Annual Rainfall in Erbil Governorate},
  journal = {Journal of Geoscience and Earth Observation},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {158-171},
  doi = {10.62762/JGEO.2026.171666},
  url = {https://www.icck.org/article/abs/JGEO.2026.171666},
  abstract = {Accurate spatial estimation of rainfall is critical for hydrological modeling, water resource management, and agricultural planning---particularly in mountainous and semi-arid regions with sparse monitoring networks. This study presents an Enhanced Elevation-Weighted Local Regression (EWLR) model to generate a high-resolution (30~m) annual rainfall surface for Erbil Governorate, northern Iraq. The EWLR model integrates distance weighting, elevation similarity weighting, and orographic enhancement within a locally weighted regression framework. Average annual rainfall, derived from rainy seasons spanning 1997--1998 to 2024--2025 across 19 meteorological stations, along with a 30~m resolution digital elevation model (DEM), were used to construct and validate the model. Hyperparameters were optimized via Leave-One-Out Cross-Validation (LOOCV), and performance was benchmarked against conventional methods including Inverse Distance Weighting (IDW), Kriging, Thin-Plate Spline, and Radial Basis Function interpolation. Results indicate that EWLR outperforms all benchmarks, achieving \$R^2 = 0.797\$, RMSE \$= 120.9\$~mm, and MAE \$= 87.46\$~mm. Rainfall shows a strong positive correlation with elevation (\$r = 0.907\$, \$p < 0.001\$), increasing nearly fivefold from lowland plains (\$\sim\$270~mm) to mountainous areas (\$>\$1{,}300~mm). The final high-resolution rainfall map captures orographic effects reasonably well, providing a physically consistent, statistically robust dataset suitable for hydrological, climatic, and environmental modeling in data-sparse mountainous regions. The methodology offers a reproducible, elevation-centric framework adaptable to other elevation-driven variables (e.g., temperature lapse rates or snow accumulation) and complex terrains with limited observations.},
  keywords = {rainfall interpolation, Elevation-Weighted Local Regression (EWLR), orographic enhancement, Kurdistan Region, mountainous terrain, spatial precipitation mapping, hydrological modeling},
  issn = {3144-2692},
  publisher = {Institute of Central Computation and Knowledge}
}