Forecasting and zoning flood potential according to climate change algorithms (Case Study: Garganrood Watershed)

Document Type : Article extracted From phd dissertation

Authors

1 Phd student of Geography and climatology, Noor branc, Islamic Azad University, Noor, Iran.

2 Assisitant Professor of Geography, Noor branc, Islamic Azad University, Noor, Iran.

3 Associate Professor of Geography, Noor branc, Islamic Azad University, Noor, Iran.

4 Professor of Earth Sciences, Shahid Beheshti University,Tehran, Iran.

Abstract

Extended Abstract
Introduction
Flood is one of the natural phenomena and one of the most important and destructive hazards in the world, which is associated with loss of life and property every year in different parts of the world and Iran. Climate change has consequences and effects on global warming, reduction of  Agricultural production, changes in the diversity and vegetation of pastures, changes in groundwater levels, the occurrence of social and economic problems, and so on. The overall goal of this research is to zoning and model areas at risk and  flood risk for changing climates. The prevailing approach of the research is the approach of climate change and its effects on the hydrology of Gorganrood watershed. The key goal of the current research is to model and forecast regional flood risk under  climate   change conditions using fuzzy analysis algorithm, hierarchical analysis and SWAT model in the watersheds of Golestan province. In fact, it is possible to plan the water resources of the region more precisely and help to calculate the more precise management of the transfer of the region along with other environmental variables, including this research. There have been many research studies on the phenomenon of precipitation in Golestan province and Iran, but none of them have examined this climatic variable with the perspective of climate change together with environmental variables.                                                                                                      
Methodology
Gorganrood Basin with an area of ​​10197 square kilometers is one of the northeastern basins of the country, a large part of which is located in Golestan province. It is bounded by the Atrak catchment area and the Caspian Sea and Qarahsu catchment area from the west.                                                                                                                                     In the present study, flood zone changes in Gorganrood watershed based on the use of meteorological organization (synoptic) station information with a 30-year statistical period (1989 to 2018), land use, vegetation, topographic moisture index, slope, altitude, land lithology, Distance from river, river density, erosion, soil science, runoff, simulated data The average rainfall of HadCM3 model in LARS-WG under SRA1B scenario is estimated between 2011 and 2045. The evaluation criteria used in this study are defined based on parameters such as ME, RMSE, ASE, MSE. The most important criterion for estimating estimates is the square root of the mean error (RMSE). (ME) is the mean of the errors or the mean difference between the estimated value and the value observed at point I. The SWAT model is an extension under ArcGIS software that simulates the main hydrological processes including evapotranspiration, surface runoff, deep infiltration, groundwater flow and subsurface flows by the simulator model. The type of land use in the region, the type of vegetation in the region, the slope values ​​and the length of the slope in different areas, which is used through the Geographic Information System (GIS). The input spatial variables of the SWAT model in this study include the digital elevation model (DEM) information layers, the soil layer with soil texture information, and the land use layer with its descriptive information. Hydroclimatology and numerical variables of SWAT model include daily precipitation, minimum and maximum temperature related to stations inside and outside the basin and the average daily runoff of the basin outlet along with their geographical locations. Simulating large and complex areas with different management strategies can be done without spending a lot of time and money.                  
Results and discussion
The results for SRA1B scenario therefore it can be concluded that in general for the next period, the  area under study will face a decrease in average flow rate. By analyzing the obtained results, it can be inferred that the phenomenon of climate change will have tangible effects on the studied  area over time and will change the values ​​of temperature and precipitation parameters and will reduce winter precipitation and increase the temperature of the studied area. results of the future discharge simulation show a decrease in runoff for October under the SRA1B scenario. Therefore, according to the results, it can be stated that in general, the SRA1B scenario predicts a decrease in discharge for the next period compared to the observed discharge. The simulation results of the basin output flow and observational flow measured by architect R2 and NS as well as the uncertainty parameters r-factor, pfactor were evaluated and analyzed. The optimal values ​​of R2 and NS coefficients are one and one. One of the goals of the SWAT model is to To reduce impotence. So that most of the observational data are at the level of 95%. In research, NS coefficient greater than 0.5 and p-factor greater than 0.5 have been introduced as satisfactory values. Examining the changes, it is expected that the runoff will decrease with increasing scenarios. This trend has decreased in RCP4.5, although its limit rainfall has also increased, and in RCP8.5 (with more rainfall) the runoff has increased. The reason for this change is that the incidence of precipitation in RCP4.5 was in summer and early autumn, which despite the high evaporation and lack of snowmelt (according to RCP8.5 which in spring coincides with snowmelt) runoff is less than the next  scenario.                             
Conclusion
Flood risk zoning in the Gorganrood watershed, 345 villages with a population of 275,312 people are at high risk of flooding. square, it has a relatively low potential equal to 1425.46 square kilometers, a very high degree of flood risk equal to 884.68 square kilometers, and finally, an area with a low flood potential equal to 623.12 square kilometers from the entire surface of the Gorganrood watershed. It can also be concluded that most of the basin level equal to 89% is at risk of severe flooding. In all the stations, according to the predictions made by the LARS-WG model, the rainfall for the future scenario has increased in some months and decreased in some months compared to the base period . So that in the summer month, the rainfall will decrease and compared to the winter season, the rainfall will increase compared to the base period. The comparison of the monthly rainfall of the base period (2011-2007) with the rainfall simulated by the HadCM3 model for the SRA1B scenario (2011-2045) also shows a significant increase in rainfall in this series of scenarios in the months of September, October and November. It can be seen with a few changes. which can be investigated in terms of the effects of this phenomenon by studying the average percentage of precipitation changes for the SRA1B scenario in 2 periods; 2011-2045, 2045-2065 also shows the changes in rainfall from August in the period, 2011-2045 to 37.5% in November of the same period compared to the base period. The very high increase of precipitation changes in November can also be investigated and pondered for the cause of autumn floods.

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References

  1. Aqelpour, P. & Nadi, Me. (2017). Assessing the accuracy of SARIMA model in modeling and long-term forecasting of monthly assessment in different climates of Iran. Climatology Research, Vol.20, No. 35, pp. 113-126. [Persian].
  2. Abedini, M. & Lotfi, Kh. (2018). Estimating the height of runoff to analyze flood potential using the curve number method in the Shahrood catchment area of Ardabil. Geographical Space, Vol 19, No. 68, pp. 163-181. [Persian].
  3. Asgari, Safari, A. & Fathi, H. (2017). Investigation of flooding in the Jafarabad watershed. Applied Research Journal of Geographical Sciences, Vol.18, No. 50, pp. 77-90. [Persian].
  4. Bahri, M. &Dasturani, M. (2016). Evaluating the effects of climate change and land use change on the hydrological response of Eskandari watershed. Geography and Environmental Hazards, Vol. 6, No. 2. pp. 37-57.[Persian].
  5. Cherie NZ. Downscaling and modeling the effects of climate change on hydrology and water resources in the upper Blue Nile river basin, Ethiopia (Doctoral dissertation, Universitätsbibliothek Kassel) (2013). 329.
  6. Danumah JH. Assessing Urban Flood Risks under Changing Climate and Land Use in Abidjan District, South Cote d’Ivoire (Doctoral dissertation) (2016).197.
  7. Feloni, E., Mousadis, I.. & Baltas, E. (2020). Flood vulnerability assessment using a GIS‐based multi‐criteria approach—The case of Attica region. Journal of Flood Risk Management, 13, pp. 1-15.
  8. Junxia, Jia., Xinyi, W., Naima, A. & Wei, Z. (2019). Flood-Risk Zoning Based on Analytic Hierarchy Process and Fuzzy Variable Set Theory, Natural Hazards Review,Vol. 20, No.3.
  9. Iqbal, M.S., Dahri, Z.H., Querner, E.P., Khan, A. & Hofstra, N. (2018). Impact of Climate Change on Flood Frequency and Intensity in the Kabul River Basin.  Vol.8, No.4, pp.114-130.
  10. Ghasemian, H. & Najafi, I. (2018). Flood risk zoning in Kohdasht city using hierarchical and fuzzy analysis model. Geography and Human Relations, Vol. 2, No.3, pp. 403-417. [Persian].
  11. Guderzi, M. & Fatehifar, A. (2018). Flood risk zoning due to climate change under RCP 8.5 scenario using SWAT hydrological model in Gis environment (Azar Shahr Chai Basin). Applied Research Journal of Geographical Sciences 2018, Vol. 19, No. 53, pp. 117-99. [Persian].
  12. Ganji, K., Qara Chalo, S. & Ahmadi, A. (2019). Investigating the effect of the morphological indicators of the Gorganroud River on flood plains using remote sensing data and spatial analysis (study area: Aqqola city). Geography and Environmental Hazards, Vol. 9, No. 3, pp. 205-225. [Persian].
  13. Kim, V., Tantanee, S. & Suparta, W. (2020). Gis-based flood hazard mapping using hec-ras model: a case study of lower mekong river, cambodia, Geographia technica, Vol.15, No.1, 16-26.
  14. Kastridis, A., Kirkenidis, C. & Sapountzis, M. (2020). An integrated approach of flash flood analysis in ungauged Mediterranean watersheds using post‐flood surveys and Unmanned Aerial Vehicles (UAVs). Hydrological Processes.
  15. Khoshravesh, M., Mirnaseri, M. & Pasarkello, M. (2016). Detecting the trend of precipitation changes in the north of the country using the non-parametric Mann-Kendall test. Watershed Management Research, Vol. 8, No. 16, pp. 223-231. [Persian].
  16. Mlinsky, D., Walega, A., Stachura, T. & Kaczor, G. (2019). A New Empirical Approach to Calculating Flood Frequency in Ungauged Catchments: A Case Study of the Upper Vistula Basin, Poland.  Vol.11, No.3, pp.601-622.
  17. Mohammadi, H., Amiri, E. & Rabbani, F. (2017), Climate Change and Climate Models, University of Tehran Printing and Publishing Institute, pp. 182. [Persian].
  18. Neferzadegan, Ali Reza; Mohammadi Far, Ali Akbar; baqar Fard, Hassan & Forozan Fard, Masoumeh (2018). Integration of multi-criteria decision making models and regional flood analysis technique to prioritize sub-basins for flood control (case study: Dehbar Khorasan watershed). Geography and environmental hazards, Vol. 8, No. 2, pp. 27-45. [Persian].
  19. Shrestha S., & Lohpaisankrit W. Flood hazard assessment under climate change scenarios in the Yang River Basin, Thailand. International Journal of Sustainable Built Environment. 2017 Dec 1;6(2):285-98.
  20. Soleimani, Sahar, Salahi, Broumand. (1401). Colloquial analysis of flooding rains in the north of Iran (case study: heavy and widespread rain from October 12 to 15, 2017). Climatology researches, Vol. 1401, No.51, pp. 15-31. [Persian].
  21. Shakeri, H., Motiei, H. & McBain, E. (1401). Analyzing the performance of fuzzy microscale models and SDSM in evaluating climate changes under RCP scenarios in Tehran. Climatology Research, Vol.1401, No. 50, pp. 61-82. [Persian].
  22. A., Seejata. K., Chatsudarat, S., Chidburee., P., Mahavik, N., Kongmuang., CH. & Tantanee.S. (2019). FD Flood Hazard assessment using Fuzzy analytic hierarchy process: A case studt of Bang Rakam model in Thailand. The 40th Asian Conference on Remote Sensing (ACRS 2019) October 14-18, 2019 / Daejeon Convention Center (DCC), Daejeon, Korea.
  23. Zarei, Azin., Asadi, I., Ebrahimi, A., Jafari, M. & Malekian, Arash (2017). Investigating the changes of precipitation and temperature parameters under climatic scenarios in the pastures of Chaharmahal and Bakhtiari provinces. Marta, Vol. 12, No. 4, pp. 426-436. [Persian].
  24. Zhixiang, L., Songbing, Z., Honglang, X.,  Chunmiao, Z., Zhenliang, Y. & Weihua, W. (2015) Comprehensive hydrologic calibration of SWAT and water balance analysis in mountainous watersheds in northwest China, Physics and Chemistry of the Earth , No. 79, pp.76-85.
Geography
Volume 21, Issue 78
Autumn2023, pp.1-202.
October 2023
Pages 109-134

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