This README file was generated on 2025-12-23 by Manisha KC

Manuscript Title:
Solar Energy Landscapes in Nepal: Site suitability analysis of solar PV using a Geospatial Multi-Criteria Decision Analysis

Overview

This README describes the datasets, methods, and files associated with the manuscript submitted to an Open Royal Society journal. The study applies a GIS-based Multi-Criteria Decision Analysis (MCDA) integrated with the Analytic Hierarchy Process (AHP) to identify suitable locations for large-scale solar photovoltaic (PV) farm development across Nepal.

The datasets support spatial analysis, criteria weighting, suitability classification, and result visualization presented in the manuscript.

Authors

Manisha KC,
Department of Geography and Anthropology, Louisiana State University, USA
Renewable and Sustainable Energy Laboratory, Kathmandu University, Nepal

Geeta Bhatta,
Renewable and Sustainable Energy Laboratory, Kathmandu University, Nepal

Basant Awasthi,
Department of Geography and Anthropology, Louisiana State University, USA

Timothy Anderson,
School of Computing, Mathematics and Engineering, Charles Sturt University, Australia

Sunil Prasad Lohani (Corresponding author), Email: splohani@ku.edu.np
Renewable and Sustainable Energy Laboratory, Kathmandu University, Nepal

Study Area
Geographic extent: Nepal (entire national boundary)
Latitude/Longitude: Approximately 26°–30° N, 80°–88° E
Total area: 147,516 km²
Ecological zones: Terai plains, Hills, Himalayas

Spatial Reference and Resolution
Coordinate reference system: WGS 84 / UTM Zone 45N
Spatial resolution: 30 m × 30 m (All raster datasets were resampled to a uniform spatial resolution before analysis.)

Data Description: The submitted dataset contains processed and reclassified GIS layers generated by the authors from publicly available source datasets during the GIS-based Multi-Criteria Decision Analysis (MCDA). Each criterion was standardized into suitability classes according to the methodology described in the manuscript.

The dataset includes the following processed layers:

1. Reclassify_GHI
2. Reclassify_Aspect
3. Reclassify_Slope
4. Reclassify_Elevation
5. Reclassify_Landcover
6. Reclassify_Road
7. Reclassify_Substation
8. Reclassify_ProtectedArea
9. Reclassify_River
10. Reclassify_Airport
11. Reclassify_Settlement

Methodology Summary:

Data Pre-processing:
The following steps were applied to prepare the datasets for analysis:
Projection of all datasets to a common CRS (WGS 84 / UTM Zone 45N).
Resampling of raster layers to 30 m spatial resolution.
Creation of buffer zones for constraint layers, including settlements, roads, rivers, protected areas, airports, and substations.
Reclassification of each criterion to a standardized suitability scale ranging from 0 (restricted) to 5 (very highly suitable).

Criteria and Suitability Standardization:
The analysis incorporates 11 criteria grouped into four categories:
Topographic: slope, aspect, elevation
Climatic: global horizontal irradiance (GHI)
Environmental: land use, rivers, protected areas
Economic: distance to roads, settlements, substations, airports
Each criterion was standardized using thresholds defined in the manuscript (Tables 3 and 4) and informed by previous GIS-MCDA solar siting studies.

Weight Determination (AHP):
Criteria weights were calculated using the Analytic Hierarchy Process (AHP).
Pairwise comparison matrices were constructed using Saaty’s scale (1–9).
The Consistency Ratio (CR) was calculated to validate judgment consistency.
Final CR value = 0.07, which is below the accepted threshold of 0.10, indicating acceptable consistency.

Suitability Analysis:
A weighted overlay analysis was performed in a GIS environment:
Each standardized raster layer was multiplied by its AHP-derived weight.
All weighted layers were summed to generate a composite suitability index.
The final suitability map was classified into suitability classes ranging from restricted to very highly suitable for solar PV deployment.

Folder structure and contents

• RSOS_Solar_PV_Suitability_Nepal_Data.zip: Compressed archive containing the processed and reclassified GIS layers together with the final solar PV suitability map generated for this study.

• GIS_Processed_Reclassify_Data: This folder contains the processed and reclassified raster layers generated during the GIS-MCDA analysis. These analytical layers were produced from publicly available source datasets following preprocessing, standardization, and suitability classification. The processed and reclassified layers include:

1. Reclassify_GHI
2. Reclassify_Aspect
3. Reclassify_Slope
4. Reclassify_Elevation
5. Reclassify_Landcover
6. Reclassify_Road
7. Reclassify_Substation
8. Reclassify_ProtectedArea
9. Reclassify_River
10. Reclassify_Airport
11. Reclassify_Settlement

• Final_Suitability_map: This folder contains the final solar photovoltaic (PV) suitability raster outputs generated using weighted overlay analysis, including classified suitability maps corresponding to the results reported in the associated manuscript.

File Inventory
RSOS_Solar_PV_Suitability_Nepal_Data.zip
This compressed archive contains processed and reclassified GIS layers for the eleven suitability criteria together with the final solar PV suitability map used in the manuscript.

Software Requirements:
ArcGIS Desktop/ Pro (spatial analysis and visualization)
Microsoft Excel or equivalent (AHP matrix calculations)

Reproducibility Notes:
Buffer distances and suitability thresholds follow values reported in the manuscript (Tables 4–5).
All raster layers were normalized to a common resolution before analysis.
Weighting was derived using Saaty’s pairwise comparison scale.
Results may vary slightly if alternative datasets or resolutions are used.
The dataset can be reused for renewable energy planning, spatial decision support, land-use analysis, and policy-oriented research in Nepal and other regions.

Output Files

The dataset includes:

• Reclassified suitability layers for each criterion
• Final solar PV suitability map

Limitations: The analysis relies on publicly available datasets with varying spatial resolutions.
Ground validation was not conducted due to national-scale coverage.
Resampling and standardization may introduce uncertainty at local scales.
Results represent current conditions and do not account for future policy or climate changes.

Licensing and Use: Original source datasets remain subject to the licensing terms of their respective providers. The submitted dataset contains processed and reclassified analytical layers generated for this study. This dataset is submitted to Dryad under the CC0 Public Domain Dedication (CC0). Citation of the associated manuscript is recommended as good scholarly practice but is not required as a condition of data reuse.

Citation: Users are encouraged to cite the associated manuscript when using or building upon these data.