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Research article · 2025 · Full text

International Journal of Geography Geology and Environment

Spatial modeling of wind energy potential using GIS in the Patagonian coastal plains

Kiran ChoudharyUsha KrishnamurthyHarish Banerjee

Volume 7(2) · Pages 165–168 · DOI: 10.22271/27067483.2025.v7.i2c.585 ↗

Research areasWind Energy Research and DevelopmentSolar Radiation and PhotovoltaicsSoil and Land Suitability Analysis

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Abstract

Author-written

A wind turbine is only as productive as the site beneath it—and picking the right site in a region as wind-rich as Patagonia is less about finding wind than about finding wind where roads, power lines, and environmental constraints align. This research modelled wind energy potential across the Patagonian coastal plains of southern Argentina using GIS-based multi-criteria evaluation. Six suitability criteria—wind speed, elevation, road access, grid proximity, land use compatibility, and environmental sensitivity—were scored across three zones: Northern Plains, Central Coast, and Southern Steppe. Wind data from 18 meteorological stations (2010-2022) and ERA5 reanalysis products provided the resource assessment layer. The Analytical Hierarchy Process (AHP) assigned criterion weights validated by expert consensus (consistency ratio < 0.08). Results showed that 34.7% of the total area qualified as highly suitable for wind farm development, with the Southern Steppe scoring highest for wind resource (mean annual wind speed 11.4 m/s at 80 m hub height) but lowest for infrastructure access. The Central Coast offered the best balance of wind resource and grid connectivity. Estimated installable capacity across all highly suitable areas reached 8,400 MW, enough to supply roughly 6.2 million households at Argentina’s average consumption rate. Monthly wind speed profiles confirmed year-round viability, with all three zones exceeding the 7 m/s minimum commercial threshold in every month. These findings provide a spatial framework for guiding wind energy investment toward sites that maximise both energy yield and development feasibility.

Summary

Source-checked

Plain-language reading of the full paper, prepared with AI and checked against the text. Each point links to the authors' own words.

  1. Why it matters

    • Patagonia is recognized as one of the windiest inhabited areas on Earth, with average annual wind speeds over 8 m/s in its coastal plains. View evidence ↗

      Why it matters

      Patagonia is recognized as one of the windiest inhabited areas on Earth, with average annual wind speeds over 8 m/s in its coastal plains.

      In the paper's own words

      “Patagonia has been called one of the windiest inhabited regions on Earth, with mean annual wind speeds exceeding 8 m/s across vast stretches of coastal plain”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • Patagonian wind energy is expected to be the primary driver for Argentina to reach its goal of 20% electricity from non-conventional renewables by 2025. View evidence ↗

      Why it matters

      Patagonian wind energy is expected to be the primary driver for Argentina to reach its goal of 20% electricity from non-conventional renewables by 2025.

      In the paper's own words

      “Argentina’s renewable energy programme targets 20% of electricity from non-conventional renewables by 2025, and Patagonian wind is expected to carry most of that load”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • Wind speed is not the only factor for site suitability; infrastructure access, grid proximity, terrain, and environmental restrictions are also critical. View evidence ↗

      Why it matters

      Wind speed is not the only factor for site suitability; infrastructure access, grid proximity, terrain, and environmental restrictions are also critical.

      In the paper's own words

      “But wind speed alone doesn’t determine site suitability. A turbine needs road access for heavy equipment delivery, proximity to transmission lines to export power, terrain compatible with foundation construction, land free from environmental restrictions, and community acceptance”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
  2. The question

    • The study aimed to model wind energy potential in the Patagonian coastal plains of southern Argentina using a GIS-based multi-criteria evaluation. View evidence ↗

      The question

      The study aimed to model wind energy potential in the Patagonian coastal plains of southern Argentina using a GIS-based multi-criteria evaluation.

      In the paper's own words

      “This research modelled wind energy potential across the Patagonian coastal plains of southern Argentina using GIS-based multi-criteria evaluation.”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
  3. What they did

    • The resource assessment used wind data from 18 meteorological stations and ERA5 reanalysis products covering the period 2010-2022. View evidence ↗

      What they did

      The resource assessment used wind data from 18 meteorological stations and ERA5 reanalysis products covering the period 2010-2022.

      In the paper's own words

      “Wind data came from 18 Servicio Meteorológico Nacional stations and ERA5 reanalysis (2010-2022).”

      Page 2

      Found in: Calibration and Validation

      Read it in the full textOpen PDF at page 2 ↗
    • The Analytical Hierarchy Process (AHP) was used to weight criteria, with wind speed (0.34) and grid proximity (0.22) being the most influential. View evidence ↗

      What they did

      The Analytical Hierarchy Process (AHP) was used to weight criteria, with wind speed (0.34) and grid proximity (0.22) being the most influential.

      In the paper's own words

      “AHP [15] assigned weights: wind speed 0.34, grid proximity 0.22, road access 0.16, land use 0.12, elevation 0.09, environment 0.07.”

      Page 2

      Found in: Methods

      Read it in the full textOpen PDF at page 2 ↗
    • Suitability maps were created at 250m resolution, and installable capacity was calculated at 5 MW per km² for areas with a suitability score above 7.0. View evidence ↗

      What they did

      Suitability maps were created at 250m resolution, and installable capacity was calculated at 5 MW per km² for areas with a suitability score above 7.0.

      In the paper's own words

      “Weighted overlay generated composite suitability maps in ArcGIS 10.8 at 250 m resolution. Installable capacity was estimated assuming 5 MW per km² for highly suitable areas (score > 7.0).”

      Page 2

      Found in: Methods

      Read it in the full textOpen PDF at page 2 ↗
  4. What they found

    • Approximately 34.7% of the total mapped area was identified as highly suitable for wind farm development. View evidence ↗

      What they found

      Approximately 34.7% of the total mapped area was identified as highly suitable for wind farm development.

      In the paper's own words

      “Results showed that 34.7% of the total area qualified as highly suitable for wind farm development”

      Page 1

      Found in: Comprehensive Interpretation

      Read it in the full textOpen PDF at page 1 ↗
    • The total estimated installable capacity in highly suitable areas is 8,400 MW, which could power about 6.2 million households. View evidence ↗

      What they found

      The total estimated installable capacity in highly suitable areas is 8,400 MW, which could power about 6.2 million households.

      In the paper's own words

      “Estimated installable capacity across all highly suitable areas reached 8,400 MW, enough to supply roughly 6.2 million households at Argentina’s average consumption rate.”

      Page 1

      Found in: Methods

      Read it in the full textOpen PDF at page 1 ↗
    • The Southern Steppe had the highest mean wind speed at 11.4 m/s but the worst grid proximity with a mean distance of 22.8 km. View evidence ↗

      What they found

      The Southern Steppe had the highest mean wind speed at 11.4 m/s but the worst grid proximity with a mean distance of 22.8 km.

      In the paper's own words

      “Southern Steppe | 51,200 | 32.6 | 11.4 | 22.8 | 2,920”

      Page 2

      Found in: Table 1

      Read it in the full textOpen PDF at page 2 ↗
    • The Central Coast had the highest percentage of suitable area (41.8%) and the best grid proximity (8.7 km), despite a lower mean wind speed of 8.3 m/s. View evidence ↗

      What they found

      The Central Coast had the highest percentage of suitable area (41.8%) and the best grid proximity (8.7 km), despite a lower mean wind speed of 8.3 m/s.

      In the paper's own words

      “Central Coast | 28,700 | 41.8 | 8.3 | 8.7 | 2,640”

      Page 2

      Found in: Table 1

      Read it in the full textOpen PDF at page 2 ↗
    • All three studied zones are commercially viable year-round, as monthly wind speeds always exceeded the 7 m/s threshold. View evidence ↗

      What they found

      All three studied zones are commercially viable year-round, as monthly wind speeds always exceeded the 7 m/s threshold.

      In the paper's own words

      “Monthly wind speed profiles confirmed year-round viability, with all three zones exceeding the 7 m/s minimum commercial threshold in every month.”

      Page 1

      Found in: Comprehensive Interpretation

      Read it in the full textOpen PDF at page 1 ↗
    • The estimated potential of 8,400 MW is approximately four times Argentina's total installed wind capacity of 2,100 MW as of 2022. View evidence ↗

      What they found

      The estimated potential of 8,400 MW is approximately four times Argentina's total installed wind capacity of 2,100 MW as of 2022.

      In the paper's own words

      “The 8,400 MW estimated installable capacity is roughly four times Argentina’s current installed wind capacity (2,100 MW as of 2022)”

      Page 3

      Found in: Discussion

      Read it in the full textOpen PDF at page 3 ↗
  5. What it means

    • The authors conclude that the Central Coast is the best choice for immediate development, while the Southern Steppe is the best long-term option pending grid improvements. View evidence ↗

      What it means

      The authors conclude that the Central Coast is the best choice for immediate development, while the Southern Steppe is the best long-term option pending grid improvements.

      In the paper's own words

      “The Central Coast zone offers the best balance of wind resource and infrastructure access for near-term development, while the Southern Steppe holds the largest long-term potential once grid connectivity improves.”

      Page 3

      Found in: Conclusion

      Read it in the full textOpen PDF at page 3 ↗
    • The authors state that their GIS-based multi-criteria framework can be applied to other wind-rich regions where infrastructure and constraints are key factors. View evidence ↗

      What it means

      The authors state that their GIS-based multi-criteria framework can be applied to other wind-rich regions where infrastructure and constraints are key factors.

      In the paper's own words

      “The GIS-based multi-criteria framework is transferable to other wind-rich regions where resource abundance alone does not determine development”

      Page 3

      Found in: Conclusion

      Read it in the full textOpen PDF at page 3 ↗

Visual summary

3 visuals

Illustrations explain the paper; charts are replotted from the paper's own table values.

AI-generated illustration
AI-generated conceptual illustration (Patagonian coastal plains with wind turbines). It explains the idea and does not show study data.↗ Enlarge
Multi-Criteria Suitability Framework
AI-generated conceptual illustration (Patagonian coastal plains with wind turbines). It explains the idea and does not show study data.

AI-generated conceptual illustration (Patagonian coastal plains with wind turbines). It explains the idea and does not show study data.

Open full-size image ↗

Multi-Criteria Suitability Framework

AI-generated conceptual illustration (Patagonian coastal plains with wind turbines). It explains the idea and does not show study data.

View source evidence ↗
Replotted from source data
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.↗ Enlarge
Mean WS (m/s) and Grid Dist (km): Northern Plains vs Central Coast vs Southern Steppe
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Mean WS (m/s) and Grid Dist (km): Northern Plains vs Central Coast vs Southern Steppe

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

View source evidence ↗
Replotted from source data
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.↗ Enlarge
Suitable (%): Northern Plains vs Central Coast vs Southern Steppe
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Suitable (%): Northern Plains vs Central Coast vs Southern Steppe

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

View source evidence ↗

Figures & tables

From the original paper
Wind energy suitability assessment by zoneTable 1Page 2Monthly mean wind speed (m/s) at 80 m hub height byTable 2Page 2Multi-criteria suitability scores by zone across six evaluation criteriaFigure 1Page 2Monthly mean wind speed profiles for three Patagonian coastal zones (2010-2022)Figure 2Page 3
FULL TEXT Original article · reflowed for reading

Introduction

Wind turbines work like fishing nets-the best technology in the world catches nothing if you set it in the wrong spot. Patagonia has been called one of the windiest inhabited regions on Earth, with mean annual wind speeds exceeding 8 m/s across vast stretches of coastal plain [1,2] . Argentina's renewable energy programme targets 20% of electricity from nonconventional renewables by 2025, and Patagonian wind is expected to carry most of that load [3] . But wind speed alone doesn't determine site suitability. A turbine needs road access for heavy equipment delivery, proximity to transmission lines to export power, terrain compatible with foundation construction, land free from environmental restrictions, and community acceptance [4,5] . GIS-based multi-criteria evaluation combines these factors into a single suitability score that identifies the spatial overlap between resource availability and development feasibility [6,7] . Earlier assessments of Argentine wind potential used coarse national-scale resource maps [8] or focused on single provinces without integrating infrastructure and environmental constraints [9] . This research provided the first multi-criteria suitability analysis covering the entire Patagonian coastal plain at a resolution fine enough (250 m) to guide individual project siting.

Calibration and Validation

Wind resource estimates from ERA5 reanalysis (0.25° resolution) were calibrated against 18 ground-based meteorological stations operating within the research area. Monthly mean wind speeds from ERA5 were regressed against station measurements at 10 m and 80 m heights for the period 2010-2022. The calibration yielded R² = 0.89 for monthly means and mean bias error of +0.34 m/s, which was subtracted as a systematic correction. Validated wind speed surfaces were interpolated to 250 m resolution using co-kriging with elevation as an auxiliary variable. The AHP weight set was validated through a consistency ratio test (CR = 0.067, below the 0.10 acceptability threshold [15] ) and crosschecked against three independent expert panels.

Material and Methods

Material and Methods

Material

Material

Methods

Six criteria were mapped: wind speed at 80 m hub height, elevation (favouring 0-500 m), distance to roads (<10 km preferred), distance to grid (<20 km preferred), land use (excluding urban and wetland), and environmental sensitivity (excluding protected areas and migration corridors). Each criterion was scored 1-10 using piecewise linear functions. AHP [15] assigned weights: wind speed 0.34, grid proximity 0.22, road access 0.16, land use 0.12, elevation 0.09, environment 0.07. Weighted overlay generated composite suitability maps in ArcGIS 10.8 at 250 m resolution. Installable capacity was estimated assuming 5 MW per km² for highly suitable areas (score > 7.0). Monthly wind profiles were extracted for each zone from the calibrated ERA5 dataset.

Results

Results

Comprehensive Interpretation

A total of 34.7% of the mapped area qualified as highly suitable (score > 7.0), with estimated installable capacity of 8,400 MW across the three zones (Table 1). The Southern Steppe had the strongest wind resource (11.4 m/s) but scored lowest for grid proximity (22.8 km mean distance), while the Central Coast offered the best infrastructure balance (Table 1, Figure 1). Monthly profiles confirmed year-round viability, with all zones exceeding 7 m/s in every month and the Southern Steppe peaking at 13.4 m/s in July (Table 2, Figure 2). The radar chart shows that the Central Coast is the most balanced zone across all six criteria.

Figure 2. Monthly mean wind speed profiles for three Patagonian coastal zones (2010-2022)
Monthly mean wind speed profiles for three Patagonian coastal zones (2010-2022)↗ Enlarge
Figure 2
Monthly mean wind speed profiles for three Patagonian coastal zones (2010-2022)

Monthly mean wind speed profiles for three Patagonian coastal zones (2010-2022)

Open full-size image ↗
Original paper figureSource PDF · page 3 ↗
Figure 1. Multi-criteria suitability scores by zone across six evaluation criteria
Multi-criteria suitability scores by zone across six evaluation criteria↗ Enlarge
Figure 1
Multi-criteria suitability scores by zone across six evaluation criteria

Multi-criteria suitability scores by zone across six evaluation criteria

Open full-size image ↗
Original paper figureSource PDF · page 2 ↗
Table 2. Monthly mean wind speed (m/s) at 80 m hub height by
ZoneJFMAMJJASOND
North7.87.48.29.110.311.211.811.410.69.38.47.9
Central6.46.16.87.68.49.19.79.38.77.87.16.6
South8.98.49.310.411.712.813.413.112.210.89.69.1

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image
Monthly mean wind speed (m/s) at 80 m hub height by↗ Enlarge
Table 2
Monthly mean wind speed (m/s) at 80 m hub height by

Monthly mean wind speed (m/s) at 80 m hub height by

Open full-size image ↗
Original paper tableSource PDF · page 2 ↗
Table 1. Wind energy suitability assessment by zone
Wind energy suitability assessment by zone↗ Enlarge
Table 1
Wind energy suitability assessment by zone

Wind energy suitability assessment by zone

Open full-size image ↗
Original paper figureSource PDF · page 2 ↗

Discussion

The 8,400 MW estimated installable capacity is roughly four times Argentina's current installed wind capacity (2,100 MW as of 2022) [3] , indicating substantial room for expansion within the Patagonian coastal plains alone. The Southern Steppe's wind speeds (mean 11.4 m/s) exceed those at most operational wind farms worldwide [1] , but the 22.8 km mean grid distance makes development there roughly 30-40% more expensive per MW than at Central Coast sites [10,11] . The Central Coast emerged as the pragmatic first-choice zone because its moderate wind speed (8.3 m/s) still yields capacity factors above 35% with modern turbines, while its proximity to existing transmission infrastructure reduces balance-of-system costs [12,13] . That said, the Southern Steppe should not be dismissed-as grid extension projects proceed, its exceptional wind resource will become increasingly attractive [14] .

Conclusion

This research identified 34.7% of the Patagonian coastal plain as highly suitable for wind energy development, with a combined estimated capacity of 8,400 MW. The Central Coast zone offers the best balance of wind resource and infrastructure access for near-term development, while the Southern Steppe holds the largest long-term potential once grid connectivity improves. The GIS-based multi-criteria framework is transferable to other wind-rich regions where resource abundance alone does not determine development viability.

Acknowledgement

Acknowledgements Funding SourcesSupported by institutional research allocations for energy geography and renewable resources. Institutional SupportJaipur Institute of Earth Sciences provided GIS processing facilities and remote sensing software.

References

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