Spatial analysis of meteorological drought susceptibility with a forest and watershed monitoring approach in Kurdistan Province

Document Type : Scientific article

Authors

1 Ph.D. Student of Watershed Management, Faculty of Natural Resources, `Urmia University, Urmia, I. R. Iran

2 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, I. R. Iran

3 Postdoctoral researcher, University of Prince Edward Island, Canada

Abstract

Background and Objective: Meteorological drought, as one of the most catastrophic types of drought, has direct and indirect destructive impacts on society and can even influence the occurrence of other natural phenomena. Iran, due to its geographical position in the arid and semi-arid belt, is highly prone to drought, having experienced frequent and severe droughts in watersheds and forested areas over the past three decades. Accurate spatial prediction (identification of starting points) and temporal forecasting of potential drought damages at both global and national levels is highly challenging. However, in a general assessment, research has shown that damages caused by meteorological drought are more pronounced in forested areas compared to watersheds. Identifying influencing factors, measuring, predicting, and defining a natural hazard such as drought is often very difficult and impractical due to its complexity. Therefore, implementing adaptive measures tailored to the region requires the identification of key components, recognition of complex relationships, and a comprehensive understanding of influencing factors, along with the assessment of drought risks through the preparation of drought vulnerability and sensitivity maps.
Material and Methods: The geographical location of Kurdistan Province is between 34°44′ and 36°30′ N latitude and 45°31′ to 48°16′ E longitude from the Greenwich meridian. A map of drought distribution points was prepared using the Drought Severity Index (DSI) in the Google Earth Engine environment. Natural, anthropogenic, and morphometric factors involved in drought occurrence were considered. The digital elevation model was downloaded from the USGS website and used to prepare information layers of elevation, slope, and morphometric indices. Layers of precipitation, temperature, evapotranspiration, and climate were prepared from data of rain gauge, synoptic, and climatological stations across the province and interpolated using the kriging method. The Normalized Difference Vegetation Index (NDVI) was extracted from Sentinel-2 imagery, and the soil map of Kurdistan Province was obtained from the FAO soil database. Information layers were prepared in ArcGIS and SAGA_GIS software. The drought sensitivity map was generated using a hybrid machine learning model (Bagging–Random Forest). The contribution of each factor to drought occurrence was evaluated using the permutation index, and the model performance was assessed with the ROC curve in the R programming environment.
Results: The results of model evaluation using the ROC curve after preparing the drought sensitivity map with the Bagging–Random Forest method show that the model has excellent performance (AUC > 0.9) in identifying sensitive areas. The analysis of the percentage of areas with sensitivity and vulnerability within the study area indicated that about 60% of watersheds and forested areas in the province fall within the range of moderate to very high vulnerability. The results of the permutation analysis for the model in determining the factors influencing drought occurrence in Kurdistan Province revealed that climatic factors such as precipitation, temperature, climate, and evapotranspiration had the highest influence, whereas the NDVI and the mass balance index had the lowest impact.
Conclusion: The results of the hybrid Bagging–Random Forest model for Kurdistan Province showed that highly drought-sensitive areas are mainly located in the eastern and partially southern parts of the province. Since most of the forested watersheds and valuable rangelands are situated in the western parts of the province, their sensitivity to drought is relatively lower compared to other regions. However, the evaluation of the model’s predictive map in western areas, which include high-quality forests and rangelands, indicated that forested watersheds are in a more critical condition than other watersheds. Therefore, priority in future planning should be given to these areas.

Keywords

References

Ahmadalipour, A.; Moradkhani, H.; Multi-dimensional assessment of drought vulnerability in Africa: 1960–2100. Science of the Total Environment 2018, 644, 520-535.‏
Ahmadi, A.; Noorali, H.; Campana, M.; Barroudi, M.; Analyzing the hydropolitical tensions of the Hirmand/Helmand River through the evaluation of land use changes using the SVM method. Human Geography Research 2023, 55(4), 225-243. (In Persian)
Ahmed, K.; Shahid, S.; Chung, E. S.; Wang, X. J.; Harun, S. B.; Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan. Journal of Hydrology 2019, 570, 473-485.‏
Al-Qubati, A.; Zhang, L.; Pyarali, K.; Climatic drought impacts on key ecosystem services of a low mountain region in Germany. Environmental Monitoring and Assessment 2023, 195(7), 800.‏
Anderegg, W.R.L.; Anderegg, L.D.L.; and Huang, C.; Testing Early Warning Metrics for Drought-Induced Tree Physiological Stress and Mortality. Global Change Biology 2019, 25: 2459–69.
Ansari, A.; Rao, K. S.; Jain, A. K.; Ansari, A.; Formulation of multi-hazard damage prediction (MhDP) model for tunnelling projects in earthquake and landslide-prone regions: A novel approach with artificial neural networking (ANN). Journal of Earth System Science 2023, 132(4), 164.‏
Azevedo, B. F.; Rocha, A. M. A.; Pereira, A. I., Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning 2022, 1-43.‏
Azizi, M.; Rostami, F.; & Heidari, R. H;. Evaluation of livelihood resilience of agroforestry system users in Paveh city in the face ‎of drought. Forest Research and Development 2023, 9(1), 29-45. )In Persian(
Bandak, I.; Karami, P.; Karaji, M. G.; Dragovich, D., Exclosure and Changes in Soil and Vegetation Properties in Different Land Utilization (1976–2021) in Kurdistan Grasslands, Iran. Journal of Soil Science and Plant Nutrition 2024, 1-13.‏
Bordbar, M.; Aghamohammadi, H.; Pourghasemi, H. R.; Azizi, Z., Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques. Scientific Reports 2022, 12(1), 1451.‏
Breiman, L., Bagging predictors. Mach Learn 1996, 24:123–140
Gerdener, H.; Engels, O.; Kusche, J., A framework for deriving drought indicators from the Gravity Recovery and Climate Experiment (GRACE). Hydrol Earth Syst Sci 2020, 24(1):227–248.
Gupta, N.; Kanungo, D. P.; Das, J., Multi-hazard susceptibility mapping of landslides and earthquakes in Bhagirathi Valley region of Uttarakhand Himalaya, India. Journal of Spatial Science 2024, 1-26.‏
Haidari, M.; Matinizadeh, M.; Pourhashemi, M.; Nouri, E.; Bagheri Delijani, N., Investigating changes in the physical and chemical characteristics of soil in control and dieback stands in Marivan county, Kurdistan province in Iran. Forest Research and Development 2024, 10(1), 95-111.
Hanifinia, A.; Abghari, H., Predicting flood-prone areas using generalized linear and maximum entropy machine learning models. Journal of Natural Environmental Hazards 2025, 14(43), 19-34. )In Persian(
Hanifinia, A.; and Nazarnejad, H.; The Effect of Morphometry Indices on Improving the Performance of Data Mining Models for Landslide Sensitivity Zoning in Cherikabad Watershed, Urmia. Journal of Geography and Environmental Hazards 2022, 10(4), 47-68. )In Persian(
IPCC., Climate Change. Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)]. IPCC, Geneva 2007, Switzerland: 104.
Jaferyan, E.; pilehvar, B.; & Tavakoli, M.; (). Physiological responses of mature Persian oak (Quercus brantii L.) under natural conditions to drought stress. Forest Research and Development 2024, 10(2), 167-181. )In Persian(
Janizadeh, S.; Bateni, S. M.; Jun, C.; Im, J.; Pai, H. T.; Band, S. S.; Mosavi, A., Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility. Geomatics, Natural Hazards and Risk 2023, 14(1), 2206512.‏
Khorrami, B.; Gündüz, O., Detection and analysis of drought over Turkey with remote sensing and model-based drought indices. Geocarto International 2022, 37(26), 12171-12193.‏
Mani, Z. A.; Khorram-Manesh, A.; Goniewicz, K., Global health emergencies of extreme drought events: Historical impacts and future preparedness. Atmosphere 2024, 15(9), 1137.‏
Mao, Y.; Nijssen, B.; Lettenmaier, D. P., Is climate change implicated in the 2013–2014 California drought? A hydrologic perspective. Geophysical Research Letters 2015, 42(8), 2805-2813.
Massoudi, M.; Goodarzi, M.; Moini, A.; Motamedvaziri, B., Evaluation and Comparison of Different Meteorological Drought Indices and Determination of Relevant Index in Fars Province. Journal of Crops Improvement 2024, 26(3), 605-620. )In Persian(
Mogholi, M., Evaluation and Modeling of Land Use Changes of Firozabad Using Multitemporal Satellate Imagery. Human Geography Research 2024, 56(2), 191-212. (In Persian)
Nazeri Tahroudi, M.; Ramezani, Y.; Ahmadi, F., Hydrogeological Drought Management Based on HDMI Multivariate Index. Environment and Water Engineering 2020, 6(4), 473-484. )In Persian(   
              Park, H.; Kim, K.; Lee, D. K., Prediction of severe drought area based on random forest: Using satellite image and topography data. Water 2019, 11(4), 705.
Rokach, L., "Ensemble-based classifiers". Artificial Intelligence Review 2010, 33 (1–2), 1–39.
Rotello, C. M.; & Chen, T.; ROC curve analyses of eyewitness identification decisions: An analysis of the   recent debate. Cognitive Research: Principles and Implications 2016, 1, 1-12.
Saha, S.; Saha, A.; Hembram, T. K.; Kundu, B.; Sarkar, R., Novel ensemble of deep learning neural network and support vector machine for landslide susceptibility mapping in Tehri region, Garhwal Himalaya. Geocarto International 2022, 37(27), 17018-17043. ‏
Sheikh Oveisi, M.; Taheri, A.; Mirlotfi, P. R., Political and security approaches to drought in Sistan and tension with Afghanistan. Geography (Regional Planning) 2024, 143-158. )In Persian(   
Singh, P.; Kannaujiya, A. K.; Deep, A.; Singh, S.; Mohanty, T.; Prakash, K., Spatio‐temporal drought susceptibility assessment of Ken River Basin, Central India, and its evaluation through river's morphometry. Geological Journal 2023, 58(2), 755-779.‏
Stephens, S.L.; Collins, B.M.; Biber, E.; and. Fulé, P.Z.; “U.S. Federal Fire and Forest Policy: Emphasizing Resilience in Dry Forests.” Ecosphere 2018, 7: 1–19.
Talukdar, S.; Mankotia, S.; Shamimuzzaman, M.; Shahfahad, Mahato, S., Wetland‐inundated area modeling and monitoring using supervised and machine learning classifiers. Advances in remote sensing for natural resource monitoring 2021, 346-365.‏  
Torabinezhad, N.; Zarrin, A.; Dadashi-Roudbari, A., Analysis of Different Types of Droughts and Their Characteristics in Iran Using the Standardized Precipitation Evapotranspiration Index (SPEI). Water and Soil 2023, 37(3), 473-486. )In Persian( 
Wang, H.; Li, Y.; Huang, G.; Zhang, Q.; Ma, Y.; Li, Y., Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought. National Science Open 2024, 3(1), 20230022.‏  
Wang, Q.; Zhao, L.; Wang, M.; Wu, J.; Zhou, W.; Zhang, Q.; Deng, M., A random forest model for drought: Monitoring and validation for grassland drought based on multi-source remote sensing data. Remote Sensing 2022, 14(19), 4981.
Yi, S.; Pei, W., Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model. Environmental Monitoring and Assessment 2024, 196(5), 1-19.‏
Yusof, M. K. T. M.; Rashid, A. S. A.; Apandi, N. M.; Khanan, M. F. B. A.; Rahman, M. Z. B. A., A review of the application of support vector machines in landslide susceptibility mapping. Disaster Advances 2023, 16(11), 71-83.
Forest Research and Development
Volume 11, Issue 2
September 2025
Pages 165-181

Files

Share

How to cite

Statistics