Application of Combination Models in Drought Monitoring using Remotely Sensed Datasets under Climate Change Scenarios: Assessing by Fuzzy Performance Criteria Mahmood Fooladi ( mahmood_fooladi@cv.iut.ac.ir, mahmoodfooladi1000@yahoo.com) Date of Submission: Department of Civil Engineering Isfahan University of Technology (IUT), Isfahan 84156-83111, Iran Degree: M.Sc. Language: Farsi Supervisor: Dr. Mohammad Hossein Golmohammadi ( m.golmohammadi@iut.ac.ir ) Supervisor: Dr. Hamid R. Safavi ( hasafavi@iut.ac.ir ) Advisor: Dr. Vijay P. Singh ( vsingh@tamu.edu ) The present study tries to provide an accurate estimate of drought performance on the Gavkhooni basin in Isfahan province using ground-based stations datasets and combining them with high-resolution remote sensing products. Combining ground-based datasets with remotely sensed products has been done using artificial intelligence models and fusion models. The advantage of the mentioned models is that they can simultaneously predict drought index with high accuracy by using ground-based observations and remotely sensed products and finally combining them together. Finally, in order to study drought behavior and its situations in the future, an attempt has been made to produce remotely sensed products based on climate change scenarios and fusion-based models for drought estimation. The final step of this research is to assess the health of the Gavkhooni basin, which uses probabilistic concepts including reliability, reversibility, and vulnerability (RRV) based on the fuzzy framework. In the present study, precipitation data related to 18 stations (9 stations from the upstream and 9 stations from the downstream of Zayandehrud dam) were selected and then the non-parametric Standardize Precipitation Index (I) in a three-time scale (3, 6, and 12 months) for each station was calculated. Then, using the k-means clustering algorithm, all stations have been Keywords: Non-parametric Drought Index, Individual Models, Fusion Models, Climate Scenarios, Health of the basin.
