Nasar-u-Minallah et al. (2025) Spatial and temporal assessment of drought dynamics in Bahawalpur (Pakistan) using remote sensing and meteorological data

Identification

• Journal: Environmental Earth Sciences
• Year: 2025
• Date: 2025-09-26
• Authors: Muhammad Nasar-u-Minallah, Nusrat Parveen, Muhammad Shahzad, Rabia Tabassum
• DOI: 10.1007/s12665-025-12520-w

Research Groups

• Institute of Geography, University of the Punjab, Lahore, Pakistan
• Department of Geography, GC University Faisalabad, Faisalabad, Pakistan
• National University of Computer and Emerging Sciences, Karachi, Pakistan

Short Summary

This study assessed spatial and temporal drought dynamics in Pakistan's Bahawalpur division (2012-2022) using remote sensing and meteorological data, identifying 2012, 2017, and 2022 as severe drought years and forecasting future temperature trends for the region.

Objective

• To evaluate spatial and temporal trends in drought occurrences and their intensity in the Bahawalpur division (Pakistan) from 2012 to 2022 using remote sensing and meteorological data.
• To quantify drought magnitude and severity using multiple drought indicators (Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Palmer Drought Severity Index (PDSI), and Normalized Difference Water Index (NDWI)).
• To determine the region's drought vulnerability, forecast future temperature trends (2023-2030) using the ARIMA model, and suggest mitigation strategies.

Study Configuration

• Spatial Scale: Bahawalpur Division, South Punjab, Pakistan (45,588 km²), including the districts of Bahawalnagar (BHN), Bahawalpur (BHP), and Rahim Yar Khan (RYK).
• Temporal Scale: Drought analysis: 2012–2022. Temperature forecasting: 2023–2030.

Methodology and Data

• Models used:
  • Drought Indices: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Palmer Drought Severity Index (PDSI), Normalized Difference Water Index (NDWI).
  • Forecasting Model: Autoregressive Integrated Moving Average (ARIMA).
  • Statistical Analysis: Pearson's correlation coefficient.
• Data sources:
  • Satellite Data: MODIS (NDVI, LST, surface reflectance for NDWI), Terra Climate dataset (PDSI).
  • Meteorological Data: NASA POWER (temperature, precipitation).
  • Crop Data: Agricultural Marketing Information Service (AMIS) (wheat, cotton, sugarcane cultivated area).
  • Platforms: Google Earth Engine (GEE), Google Colab (Python), ArcGIS.

Main Results

• The years 2012, 2017, and 2022 were identified as significant drought years based on high drought index values.
• The most severe drought, as indicated by the percentage area of VCI, occurred in 2017 (21.82%), followed by 2016 (17.99%) and 2012 (14.44%).
• Future temperature forecasts (2023-2030) using the ARIMA model predict an increasing trend for Bahawalnagar (forecasted mean: 32.86 °C) and a slight decrease for Bahawalpur (forecasted mean: 32.76 °C) and Rahim Yar Khan (forecasted mean: 33.79 °C).
• VCI maps showed extreme to severe drought conditions in 2012, 2014, 2016, 2017, 2020, and 2022, predominantly in the Bahawalpur district.
• PDSI indicated severe to moderate drought conditions across the three districts in 2012, 2014, 2017, 2018, and 2022.
• NDWI identified 2012, 2017, 2018, and 2022 as years with significant soil moisture stress.
• Correlation analysis revealed a moderate positive association between VCI and wheat yield (r = 0.59). VCI also showed strong positive correlations with NDWI (r = 0.85) and VHI (r = 0.90). TCI had the weakest correlation with wheat yield (r = 0.11).
• Rahim Yar Khan generally exhibited higher production areas for wheat, sugarcane, and cotton compared to Bahawalnagar and Bahawalpur.

Contributions

• Provides a localized, interdisciplinary framework for drought assessment in the semi-arid Bahawalpur region of South Punjab, Pakistan, integrating remote sensing, meteorological data, and statistical/machine learning techniques.
• Quantifies drought magnitude and severity using a comprehensive set of seven remote sensing-derived and meteorological indices, enhancing the understanding of drought dynamics in the region.
• Offers future temperature forecasts (2023-2030) using the ARIMA model, contributing to proactive drought management and adaptation planning.
• Identifies specific drought-vulnerable areas and periods, providing actionable insights for policymakers and stakeholders to develop targeted mitigation strategies and enhance agricultural resilience.
• Demonstrates the efficiency and utility of cloud-based platforms like Google Earth Engine for large-scale spatiotemporal drought analysis.

Funding

No funding was obtained for this study.

Citation

@article{NasaruMinallah2025Spatial,
  author = {Nasar-u-Minallah, Muhammad and Parveen, Nusrat and Shahzad, Muhammad and Tabassum, Rabia},
  title = {Spatial and temporal assessment of drought dynamics in Bahawalpur (Pakistan) using remote sensing and meteorological data},
  journal = {Environmental Earth Sciences},
  year = {2025},
  doi = {10.1007/s12665-025-12520-w},
  url = {https://doi.org/10.1007/s12665-025-12520-w}
}