per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 1 20 article Omid Bonabi Alghoo O.bonabi@urmia.ac.ir 1 Vahid Verdinejad v.verdinejad@urmia.ac.ir 2 Javad Behmanesh j.behmanesh@urmia.ac.ir 3 Tayebeh Irani t.irani@urmia.ac.ir 4 mina Rahimi mi.rahimi@urmia.ac.ir 5 Ph.D. Student, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran, Email: O.bonabi@urmia.ac.ir Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran, Email: v.verdinejad@urmia.ac.ir Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran, Email: j.behmanesh@urmia.ac.ir Former Ph.D. Student, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran, Email: t.irani@urmia.ac.ir Former Ph.D. Student, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran, Email: mi.rahimi1992@gmail.com In this study, land use change maps for the Barandoozchay watershed were prepared using TM and ETM+ sensor images from Landsat and Sentinel-2 satellites for the years 1990, 1995, 2000, 2005, 2010, 2016, 2020, and 2024 over 34 years. To predict land use for 2032, the CA-Markov model in the TerrSet software was used. The analysis employed the land use change intensity index and the integrated dynamic degree index (Ktotal) to monitor land use changes. The results indicated that the classified maps were produced with high accuracy and a Kappa coefficient greater than 0.80, indicating good reliability of the analyses. In this study, land use was categorized into six classes: irrigated lands and orchards, rangelands, rainfed lands, residential areas, water bodies, and salt marshes. Analysis of land use changes showed that the most significant changes occurred in residential areas, particularly during the periods 1995-2000 and 1995-2024, with growth rates of 22.66% and 267.35%, respectively. This growth reflects the rapid development of residential and urban areas, likely due to population growth and housing demand in the region. Additionally, during the 2020-2024 period, there was a 0.42% increase in irrigated lands and orchards, possibly due to increased demand for agricultural products and improved agricultural techniques. Predictions for 2032 indicate that the area of residential zones will increase to 95.19 km², and irrigated lands and orchards will increase to 584.64 km², while rainfed lands and rangelands will decrease. Analysis of the integrated dynamic degree index (Ktotal) also showed that the rate of land use change increased during the periods 2016-2020 and 2020-2024, while it decreased during the periods 2000-2005 and 2005-2010. These results underscore the need for sustainable natural resource management and the development of protective measures to control land use changes and maintain the environmental sustainability of the region.  http://jircsa.ir/article-1-569-en.pdf : Trend Analysis Landscape Dynamics Land Use Change Dynamic Degree Index Watershed Management per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 21 38 article Alireza Majidi Majidi.geo@gmail.com 1 Abazar Mostafaei Abazar.mostafaei@gmail.com 2 Ahad Habibzadeh Ahad_habibzadeh@yahoo.com 3 Assistant Professor, Department of Hydrology and Water Resources Development, Soil Conservation and Watershed Management Institute, Agricultural Research, Education and Extension Organization, AREEO, Tehran, Iran, Email: majidi.geo@gmail.com Research Specialist, Soil and Water Conservation Research Department, Agricultural and Natural Resources Research and Education Center of West Azerbaijan Province, Agricultural Research, Education and Extension Organization, AREEO, Urmia, Iran, Email: Abazar.mostafaei@gmail.com Assistant Professor, Soil and Water Conservation Research Department, Agricultural and Natural Resources Research and Education Center of East Azerbaijan Province, Agricultural Research, Education and Extension Organization, AREEO, Tabriz, Iran, Email: Ahad_habibzadeh@yahoo.com Iran is situated in an arid and semi-arid region characterized by negligible atmospheric precipitation and its uneven spatial and temporal distribution. Given escalating human water demands, securing sustainable water resources is paramount. Therefore, despite the country’s recurring droughts, floods, and runoff, employing flood spreading technology, a conventional, widely used, and natural solution for flood control, groundwater recharge, and vegetation enhancement, is more crucial than ever. This study evaluates the East Azerbaijan Tasuj flood spreading system’s impact on recharging the plain’s groundwater aquifer and the water table. Results show that groundwater levels in all observation wells within the East Azerbaijan Tasuj plain declined between water years 2004 and 2009, indicating a negative aquifer balance. This is attributed to increased aquifer withdrawals, reduced precipitation and rainfall, and drought conditions during this period. However, wells closest to the flood spreading networks (Pt1, Pt2, and Pt3) exhibited minimal water level changes (less than 5 meters). Considering the frequency and volume of flooding events, the flood spreading area’s size relative to the aquifer, and groundwater flow direction, this suggests the effectiveness of the networks in recharging the aquifer and partially offsetting over extraction. Analysis of precipitation amounts and flood volumes entering the spreading area revealed that rainfall exceeding 20 mm triggered flooding. Furthermore, rainfall exceeding 50 mm, or single, short-duration, high-intensity rainfall events, resulted in significant recharge, particularly during the flood spreading plan’s initial years. http://jircsa.ir/article-1-581-en.pdf Artificial recharge control volume flood control flood spreading groundwater aquifer Precipitation per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 39 60 article Fariborz Ahmadzadeh Kaleybar f.ahmadzadeh@iau.ac.ir 1 Ahad Molavi ahad.molavi@gmail.com 2 Bahman Mehrvarz Qoje Begloo mehrvarz13581365@Gmail.com 3 Assistant Professor, Department of Water Science and Engineering, Ta.C., Islamic Azad University, Tabriz, Iran, Email: f.ahmadzadeh@iau.ac.ir Assistant Professor, Department of Water Science and Engineering, Ta.C., Islamic Azad University, Tabriz, Iran, Email: ahad.molavi@iau.ac.ir Former M.Sc. Student, Department of Water Science and Engineering, Ta.C., Islamic Azad University, Tabriz, Iran, Email: mehrvarz13581365@Gmail.com The present study was conducted to evaluate the performance of artificial neural networks, support vector machine models, and their hybrid mode with the wavelet model in predicting the discharge of the Aladyzga and Arbabkandy hydrometric stations located in the Qara Su watershed. Considering the correlation index, the discharge of two months ago and the discharge of one month ago were considered as the input of the runoff model at the Arbabkandy and Aladyzga hydrometric stations, respectively. The optimal state in the artificial neural network and hybrid wavelet-artificial neural network models was achieved in two and five neurons at the Aladyzga station, respectively, and in 12 and one neuron at the Arbabkandy station, respectively. The results indicated that the agreement between the observed runoff and predicted runoff values was high when using the wavelet-artificial neural network combination and the wavelet-support vector machine combination compared to the cases of using the conventional artificial neural network and support vector machine. Thus, at Arbabkandy station, hybridizing the single model of the artificial neural network with the wavelet model increased the R parameter from 0.44 to 0.91 and also reduced the RE and RMSE parameters from 41% and 2.03 m3 s-1 to 23 % and 1.33 m3 s-1, respectively. The NSE and GMER indices in the wavelet-artificial neural network and wavelet-support vector machine models had better acceptance in both stations than in the other models, so that in Arbabkandy station, the values of these indices in the wavelet-artificial neural network model were 0.78 and 0.94, respectively. After the hybrid wavelet-artificial neural network model, which had the best fit and consistency with the observational data, the hybrid wavelet-support vector machine model had good accuracy and efficiency compared to other models used in both stations http://jircsa.ir/article-1-586-en.pdf Artificial neural network Support vector machine Wavelet transform Time delay per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 61 82 article Ebrahim amiri Dr.amiri@cfu.ac.ir 1 mohammad motamedirad m.motamedirad@cfu.ac.ir 2 ali Sadeghi a.sadeghi@cfu.ac.ir 3 Farzad Amiri Faarzaad.amiirii@gmail.com 4 Assistant Professor, Department of Geography Education, Farhangian University, Tehran, Iran, Email: Dr.amiri@cfu.ac.ir Assistant Professor, Department of Geography Education, Farhangian University, Tehran, Iran, Email: m.motamedirad@cfu.ac.ir Associate Professor of Humanities and Social Sciences Department, Farhangian University of Tehran, Iran, Email: a.sadeghi@cfu.ac.ir Former M.Sc. Student, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran. Email: Faarzaad.amiirii@gmail.com Subsidence can have many negative effects on infrastructure, the environment, and ecosystems. Therefore, an accurate and complete understanding of subsidence is essential to reduce the damage caused by the subsidence phenomenon. This research aimed to assess the subsidence rate of the Samalghan Plain using radar interferometry during the period from March 2021 to June 2024 using SNAP software. Ground data, such as piezometric wells and groundwater level drop rates during minimum and maximum periods, and exploitation wells, were used to calculate the discharge rate at the aquifer level using interpolation using the IDW method. The results of the study show that the subsidence rate in the study basin is within the desired range of 1 to 10 mm, and 35.18 percent of the basin area is in the very high critical zone and 36.88 percent is in the very critical zone, and in total, 72.06 percent of the study basin is in the very high critical and very critical subsidence zones. The subsidence rate in the very high critical zone is 4 to 10 mm, which indicates high subsidence rates in the south, center, west, and part of the north of the basin. The wells of Hyderabad-Ghale Khan Road, Mulla Hassan, Bagh Nodeh, and Ebtedaye Ghori are located in the very high critical zone. Due to its specific geological characteristics and excessive exploitation of groundwater resources, the Samalghan Plain is at risk of subsidence. To manage this phenomenon, it is necessary to take measures such as sustainable management of groundwater resources, reducing excessive groundwater extraction, artificially feeding aquifers, and carefully monitoring the water table and examining hydrogeological changes in the region to help control subsidence and prevent its increase. http://jircsa.ir/article-1-583-en.pdf Radar Semlghan Sentinel Subsidence SNAP per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 83 100 article kosar.hematjo@gmail.com 1 Hadi Memarian hadi_memarian@birjand.ac.ir 2 chezgi@birjand.ac.ir 3 j.momenidamaneh.phd@hormozgan.ac.ir 4 Former M.Sc. Student, Department of Natural Resources, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran. Associate Professor, Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran, Email: hadi_memarian@birjand.ac.ir Associate Professor, Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran, Email: chezgi@birjand.ac.ir Former Ph.D. Student, Department of Natural Resources Engineering, Faculty of Agriculture & Natural Resources, University of Hormozgan, Bandar Abbas, Iran, Email: j.momenidamaneh.phd@hormozgan.ac.ir This research aims to investigate the impacts of climate change on the amount of runoff generation in the Kashmar urban watershed by employing a combination of Barlow methods. The findings indicate that climate change, resulting from increased greenhouse gas concentrations, has significantly influenced the precipitation patterns and runoff in this semi-arid region. Among the examined General Circulation Models (GCMs) – GFDL-ESM2G, IPSL-CM5A-MR, MIROC-ESM, and NorESM1-M – the evaluations suggest that the IPSL-CM5A-MR model, with a coefficient of determination (R²) of 0.72, demonstrated higher accuracy in simulating the historical precipitation of the region. The analysis of various climate change scenarios, particularly RCP8.5 (for the period 2070-2100), reveals a substantial decrease in annual precipitation by approximately 41.12 millimeters. The Barlow method, considering the physical characteristics of the watershed such as slope, land cover type, and rainfall nature, predicts that runoff will experience a significant reduction in high greenhouse gas emission scenarios. Specifically, under the RCP8.5 scenario, a decrease of 16.7% is projected for the period 2020-2038, and a further decrease of 29.1% for the period 2070-2100. The sub-basin analysis shows that sub-basin A, with an area of 6123 hectares and characterized by E-type land (steep slope and poor vegetation cover), exhibits the highest vulnerability and generated 3351 thousand cubic meters of runoff during the baseline period. The results of the Mann-Kendall test confirm a non-significant decrease in precipitation in most scenarios, except for RCP2.6. Furthermore, a significant linear relationship between annual precipitation and maximum 24-hour precipitation, with a coefficient of determination (R²) of 0.64, was utilized in estimating the amount of runoff. Collectively, these results underscore the critical need for a fundamental revision of water resource management approaches and the development of effective strategies for adapting to the consequences of climate change in arid and semi-arid regions like Kashmar. http://jircsa.ir/article-1-585-en.pdf Climate Modeling Ecosystem Sustainability General Circulation Model (GCM) Barlow Method Downscaling Emission Scenarios per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 101 118 article Mahsa Mardani mahsa.mardani@birjand.ac.ir 1 Moein Tosan moein.tosan@birjand.ac.ir 2 Ali Nasirian a.nasirian@birjand.ac.ir 3 Mehdi Dastourani mdastourani@birjand.ac.ir 4 Ph.D. Student, Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran, Email: mahsa.mardani@birjand.ac.ir Ph.D. Student, Department of Water Science and Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran,, Email: moein.tosan@alumni.um.ac.ir; Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran, Email: a.nasirian@birjand.ac.ir Associate Professor, Department of Water Science and Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran, Email: mdastourani@birjand.ac.ir This study aimed to conduct a bibliometric analysis of the application of remote sensing in analyzing spatio-temporal rainfall changes in Iran during the period 2004 to 2024. The purpose of this analysis was to provide a macro and structured view of the research status in this field, identify publication trends, key players, and conceptual developments. Data related to articles published in the Web of Science database were analyzed using R biblioshiny and VOSviewer software. The findings of this research show that the field of application of remote sensing in rainfall estimation in Iran, with an annual growth rate of 25.20%, has high dynamism and extensive international collaborations have been formed within it. The review of highly cited articles indicates that the utilization of advanced satellite data such as GPM-IMERG, TRMM, and PERSIANN, along with downscaling methods based on non-linear relationships (such as the use of NDVI in semi-arid and humid regions) and accurate calibration techniques (using ground station data), has played a significant role in improving the accuracy of rainfall estimation. These achievements can be effective in formulating water resource management policies in Iran as follows: 1) Focusing on the use and development of infrastructure related to high-accuracy satellite data. 2) Applying advanced downscaling and calibration methods in the production of rainfall data with appropriate spatial resolution for use in hydrological models. 3) Prioritizing research focused on the analysis of rainfall climate change trends using remote sensing data. By providing this bibliometric framework, this research, while outlining the current situation, provides a basis for guiding future research and making informed decisions in the field of water resource management in Iran. http://jircsa.ir/article-1-577-en.pdf Precipitation Forecasting Bibliometric Analysis Satellite Data Hydrological Models Water Resources Management per Iranian Rainwater Catchment Systems Association Iranian Journal of Rainwater Catchment Systems 2423-5970 2783-1531 2025-08 13 2 119 139 article Abbas Sedghamiz sedghamiz@gmail.com 1 Assistant Professor, Department of Irrigation Technology, Collage of Agriculture and Natural Resources of Darab, Shiraz University, Iran, Email: sedghamiz@shirazu.ac.ir For the effective and optimal management of groundwater resources, accurate predictions considering all prevailing conditions in aquifers, particularly fluctuations in groundwater level and depth, are essential. The objective of this study, conducted in the Qotbabad region of Jahrom County, Fars Province, is to identify observation wells that provide the most reliable predictions of groundwater depth in other wells. To achieve this, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed, in combination with various training algorithms including Hybrid, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Groundwater depth data from seven observation wells across the plain were used, covering the period from October 2008 to September 2024. To evaluate model accuracy, statistical indices such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) were utilized. Based on the results, observation well No. 2 was identified as the most accurate predictor for wells No. 1 and 4, while well No. 5 was identified as the least accurate predictor for wells No. 3 and 6. Additionally, observation well No. 4, classified as a moderately accurate predictor, demonstrated the best predictive performance for wells No. 2, 5, and 7, and ranked second-best for wells No. 1, 3, and 6. This consistent ranking as either the top or second-best predictor sets well No. 4 apart from the others. Among all wells, the strongest linear relationship between observed and predicted groundwater depths was obtained for well No. 4, with an average coefficient of determination (R²) of 0.9945 across the three training algorithms. Conversely, the weakest relationship was found for well No. 3, with an average R² of 0.7435. Overall, the Hybrid method proved to be the most accurate and the fastest to execute, whereas the Genetic Algorithm method, having the most execution time, exhibited the lowest predictive accuracy. http://jircsa.ir/article-1-584-en.pdf Groundwater Adaptive Neouro- Fuzzy Inference System Training Algorithme Error Index