Exploring the Impact of Future Land Uses on Flood Risks in Python (Case study: Tangrah Basin, Golestan Province, Iran)

Document Type : Research Paper

Authors

Department of Geography, Faculty of Literature and Humanities, Kharazmi University, Tehran, Iran.

Abstract

Detection and prediction of land use and land cover (LULC) changes provide useful information to regional decision-makers for natural resource management and environmental sustainability. In this study, land use maps of the Tangrah Basin in northeastern Iran were extracted and analyzed for the past three decades (1986, 2006, and 2020) using satellite imagery and remote sensing techniques. Future land use changes for the year 2040 were then simulated using the CA-Markov integrated model under the current management process continuation scenario.The results of the CA-Markov model show that from 1986 to 2020, the most significant change was related to forest land, which decreased from 479.13 km² in 1986 to 360.16 km² in 2020. The land use map for 2040 was also predicted using the CA-Markov model, indicating a substantial increase in urban and built-up areas by 0.99% compared to 1986. To determine the impact of land use changes on flood occurrence, flood hazard zoning was carried out for two time periods (1986 and 2040). By examining the parameters affecting flood modeling in Anaconda 4.20.0 software for both scenarios, it was determined that precipitation, distance from waterways, and land use changes played effective roles in increasing peak discharge, flood volume, and high-risk points.The results demonstrate that land use change analysis can significantly contribute to water resource management and planning in flood-prone areas.
Introduction
Land use and land cover (LULC) changes are among the most critical factors influencing environmental sustainability and natural resource management. These changes, driven by population growth, urbanization, agricultural expansion, and deforestation, have profound impacts on hydrological regimes, soil erosion, biodiversity loss, and climate change. In recent decades, remote sensing technology and geographic information systems (GIS) have become indispensable tools for detecting, monitoring, and predicting LULC changes over time. The integration of satellite imagery with spatial modeling techniques enables researchers and decision-makers to understand past trends and simulate future scenarios. Among various modeling approaches, the CA-Markov (Cellular Automata-Markov Chain) model has gained widespread acceptance due to its ability to combine temporal dynamics with spatial distribution patterns. This model is particularly useful for predicting land use transitions and assessing their environmental consequences. The Tangrah Basin, located in Golestan Province, northeastern Iran, has experienced significant LULC changes over the past three decades, accompanied by an increasing frequency of devastating floods. Understanding the relationship between land use dynamics and flood susceptibility is essential for sustainable land management and disaster risk reduction in this region. This study aims to (1) detect and analyze LULC changes in the Tangrah Basin from 1986 to 2020 using satellite imagery, (2) predict future LULC changes for 2040 using the CA-Markov model, and (3) assess the impact of these changes on flood risk zoning.
Materials and Methods
The Tangrah Basin is part of the Gorganrud watershed in Golestan Province, covering an area of approximately 625 km². The region has a semi-arid to Mediterranean climate with average annual precipitation ranging from 202 mm to 903 mm across different elevations. For LULC classification, Landsat TM (1986), ETM+ (2006), and OLI-TIRS (2020) satellite images were obtained from the USGS Earth Explorer website. Pre-processing steps included radiometric and atmospheric corrections using the FLAASH module in ENVI 5.6 software. Pan-sharpening was performed using the Gram-Schmidt method to enhance spatial resolution from 30 m to 15 m for the 2020 image. Six LULC classes were defined: forest, urban/built-up areas, agricultural land, water bodies, good rangeland, and poor rangeland. Classification was conducted using the Random Forest algorithm within the EnMap-Box 2.2 plugin in ENVI 5.6. Accuracy assessment was performed using ground control points and the Kappa coefficient. For prediction of future LULC, the CA-Markov model was implemented in TERRSET2020 software. Transition probability matrices were derived from LULC maps of 1986 and 2006, and the model was validated by comparing simulated and actual LULC for 2020. For flood susceptibility mapping, ten conditioning factors were selected based on literature review and field surveys: elevation, slope, aspect, distance from streams, lithology, annual rainfall, LULC, stream power index (SPI), topographic wetness index (TWI), and drainage density. A total of 605 flood inventory points were identified using satellite imagery, historical records, and field observations. The Random Forest algorithm was implemented in Python (Spyder 3.13) within Anaconda 4.20.0 to determine the weight of each factor. The final flood hazard maps were produced in ArcGIS 10.7 and classified into five susceptibility classes: very low, low, medium, high, and very high.
Results and Discussion
The LULC classification results revealed significant changes over the 34-year study period. Forest land decreased substantially from 479.13 km² (76.6%) in 1986 to 376.63 km² (60.26%) in 2006 and 360.16 km² (57.6%) in 2020, representing a total loss of 118.97 km² (24.8%) between 1986 and 2020. Urban areas expanded from 1.85 km² (0.28%) in 1986 to 5.97 km² (0.95%) in 2020, reflecting rapid urbanization and infrastructure development. Agricultural land showed a slight decrease between 1986 and 2006 but increased to 71.19 km² (11.39%) by 2020. Good rangeland increased substantially from 81.20 km² (12.99%) in 1986 to 190.49 km² (30.47%) in 2020, largely due to forest degradation and conversion to rangeland. The Kappa coefficients for classification accuracy were 0.82, 0.85, and 0.91 for 1986, 2006, and 2020, respectively, indicating high reliability of the classification results. The CA-Markov model successfully simulated LULC for 2020 with an overall accuracy of 82.6%, and the predicted map for 2040 indicates continued forest loss (256.54 km²; 42.96%) and urban expansion (7.97 km²; 1.27%). Flood susceptibility mapping showed that very high and high flood risk zones increased by 26% from 1986 to 2040, while low and very low risk zones decreased by 19%. The Random Forest model performed excellently, with AUC values of 0.84 and 0.86 for 1986 and 2040, respectively. The most influential factors in flood modeling were distance from streams, stream power index, LULC changes, and precipitation.
Conclusion
This study demonstrates that LULC changes in the Tangrah Basin have significantly increased flood susceptibility over the past three decades, and this trend is projected to continue through 2040. The integration of remote sensing, CA-Markov modeling, and Random Forest algorithms provides a robust framework for detecting, predicting, and assessing the environmental impacts of land use dynamics. The findings highlight the urgent need for sustainable land management practices, including reforestation programs, strict land-use regulations, and integrated watershed management to mitigate future flood hazards. The methodology developed in this research can be applied to other flood-prone regions, supporting evidence-based decision-making for environmental planning and disaster risk reduction.

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Main Subjects

References

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Geography and Environmental Sustainability
Volume 16, Issue 3 - Serial Number 60
September 2026
Pages 21-47

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