Skip to main content
Log in

Flood-based critical sub-watershed mapping: comparative application of multi-criteria decision making methods and hydrological modeling approach

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

The effects of Sub-Watersheds (SWs) on each other can be more important in Flood Generation Potential (FGP). Therefore, the present study aims for prioritizing SWs based on FGP using Multi-Criteria Decision Making (MCDM) Methods including Game Theory (GT), Best-Worst Method (BWM), Analytic Hierarchy Process (AHP), Analytical Network Process (ANP), Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Analytical Network Process (FANP) and comparing its results with Hydrological Modeling Approach (HMA) in the Cheshmeh-Kileh Watershed, Iran. In GT, Condorcet algorithm were used. The best and worst criteria were identified using the BWM and compared with other criteria. In AHP and ANP, expert opinions were used and the final weight of criteria and alternative was calculated using Expert Choice and Super Decision softwares. In HMA, HEC-HMS software was used to calculate the discharge with return periods of 10- and 100-year, and finally, in all methods, FGP maps were prepared in three classes and SWs were prioritized. Based on the results of different methods, SWs 9, 2, 7, 10 and 11 were given high FGP priority. There are two possible explanations for this result. The first explanation is the difference between the values of geo-environmental criteria in each SW, and the ratio of these values and the effect of each of these criteria on the FGP. The next explanation is due to the different structural nature of each of the MCDM, which caused different prioritization of SWs based on FGP. Downstream SWs were also in a non-critical state due to dense forest cover and low slope. A comparative evaluation between the methods showed that BWM had the same result as the field evidence and HMA results and this method provided the best result. Based on SWs prioritization in BWM, high and low FGP were 33.33 and 46.67% of the study area, respectively. After BWM, GT gave a relatively good result. AHP, ANP, FAHP and FANP presented different results, but had poor performance in identifying critical areas. This study showed that optimal MCDM approaches can be used for flood management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abdo HG (2020) Evolving a total-evaluation map of flash flood hazard for hydro-prioritization based on geohydromorphometric parameters and GIS–RS manner in Al-Hussain river basin, Tartous, Syria. Nat Hazards 104:681–703. https://doi.org/10.1007/s11069-020-04186-3

    Article  Google Scholar 

  • Adhami M, Sadeghi SH (2016) Sub-watershed prioritization based on sediment yield using game theory. J Hydrol 541:977–987. https://doi.org/10.1016/j.jhydrol.2016.08.008

    Article  Google Scholar 

  • Adhami M, Sadeghi SH, Sheikhmohammady M (2018) Making competent land use policy using a co-management framework. Land Use Policy 72:171–180. https://doi.org/10.1016/j.landusepol.2017.12.035

    Article  Google Scholar 

  • Adhami M, Sadeghi SH, Duttmann R, Sheikhmohammady M (2019) Changes in watershed hydrological behavior due to land use comanagement scenarios. J Hydrol 577:124001. https://doi.org/10.1016/j.jhydrol.2019.124001

    Article  Google Scholar 

  • Aher PD, Adinarayana J, Gorantiwar SD (2014) Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: a remote sensing and GIS approach. J Hydrol 511:850–860

    Google Scholar 

  • Ahmed R, Sajjad H, Husain I (2018) Morphometric parameters-based prioritization of sub-watersheds using fuzzy analytical hierarchy process: a case study of lower Barpani watershed, India. Nat Resour Res 27:67–75

    CAS  Google Scholar 

  • Akay H, Baduna Koçyi\ugit M (2020) Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods. Soft Comput 24:14251–14263

    Google Scholar 

  • Akbari A, Samah AA, Daryabor F (2016) Raster-based derivation of a flood runoff susceptibility map using the revised runoff curve number (CN) for the Kuantan watershed, Malaysia. Environ Earth Sci 75:1–8

    CAS  Google Scholar 

  • Al-Abed N, Abdulla F, Abu Khyarah A (2005) GIS-hydrological models for managing water resources in the Zarqa River basin. Environ Geol 47:405–411

    Google Scholar 

  • Álvarez X, Gómez-Rúa M, Vidal-Puga J (2019) Risk prevention of land flood:a cooperative game theory approach

  • Amiri M, Pourghasemi HR, Arabameri A et al (2019) Prioritization of flood inundation of Maharloo watershed in Iran using morphometric parameters analysis and TOPSIS MCDM Model. Elsevier

  • Arora A, Pandey M, Siddiqui MA et al (2021) Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int 36:2085–2116

    Google Scholar 

  • Arshia A, Haghizadeh A, Tahmasebipour N, Zeinivand H (2018) Prioritization of Sezar Subbasins in terms of flooding potentian using game theory. Iran J Ecohydrol 5:1219–1231

    Google Scholar 

  • Augustin DS, Akossiwa DL, Esther DN (2019) Dry port development in Togo: a multi-criteria approach using analytic network process [ANP]. Am J Ind Bus Manag 09:1301–1317. https://doi.org/10.4236/ajibm.2019.96086

    Article  Google Scholar 

  • Avand M, Khiavi AN, Khazaei M, Tiefenbacher JP (2021) Determination of flood probability and prioritization of sub-watersheds: a comparison of game theory to machine learning. J Environ Manag 295:113040. https://doi.org/10.1016/j.jenvman.2021.113040

    Article  Google Scholar 

  • Balasubramanian A, Duraisamy K, Thirumalaisamy S et al (2017) Prioritization of subwatersheds based on quantitative morphometric analysis in lower Bhavani basin, Tamil Nadu, India using DEM and GIS techniques. Arab J Geosci 10:1–18

    Google Scholar 

  • Balogun A, Quan S, Pradhan B et al (2021) An improved flood susceptibility model for assessing the correlation of flood hazard and property prices using geospatial technology and Fuzzy-ANP. J Environ Inform 37:107–121. https://doi.org/10.3808/jei.202000442

    Article  Google Scholar 

  • Batista LFDR, Ribeiro Neto A (2022) Conceptual and analytical framework as flood risk mapping subsidy. GeoHazards 3:395–411. https://doi.org/10.3390/geohazards3030020

    Article  Google Scholar 

  • Bertola M, Viglione A, Blöschl G (2019) Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria. J Hydrol 577:123919

    Google Scholar 

  • Birkel C, Soulsby C (2015) Advancing tracer-aided rainfall–runoff modelling: a review of progress, problems and unrealised potential. Hydrol Process 29:5227–5240

    Google Scholar 

  • Blöschl G, Hall J, Parajka J et al (2017) Changing climate shifts timing of European floods. Sci 357(80):588–590

    Google Scholar 

  • Chezgi J, Vafakhah M, Falahatkar S (2020) Spatial resolution effect of remotely sensed data on flood hydrograph simulation. J Indian Soc Remote Sens 48:97–112. https://doi.org/10.1007/s12524-019-01060-z

    Article  Google Scholar 

  • Chitsaz N, Azarnivand A (2017) Water scarcity management in arid regions based on an extended multiple criteria technique. Water Resour Manag 31:233–250. https://doi.org/10.1007/s11269-016-1521-5

    Article  Google Scholar 

  • Costache R, Arabameri A, Blaschke T et al (2021) Flash-flood potential mapping using deep learning, alternating decision trees and data provided by remote sensing sensors. Sensors 21:280

    Google Scholar 

  • Daneshparvar B, Nezami SR, Feizi A, Aghlmand R (2022) Comparison of results of flood hazard zoning using AHP and ANP methods in GIS environment: a case study in Ardabil province, Iran. J Appl Res Water Wastewater 9:1–7

    Google Scholar 

  • Du J, Qian L, Rui H et al (2012) Assessing the effects of urbanization on annual runoff and flood events using an integrated hydrological modeling system for Qinhuai River basin, China. J Hydrol 464–465:127–139. https://doi.org/10.1016/j.jhydrol.2012.06.057

    Article  Google Scholar 

  • El-Zein A, Ahmed T, Tonmoy F (2021) Geophysical and social vulnerability to floods at municipal scale under climate change: the case of an inner-city suburb of Sydney. Ecol Indic 121:106988

    Google Scholar 

  • Elkind E, Lang J, Saffidine A (2011) Choosing collectively optimal sets of alternatives based on the condorcet criterion. IJCAI Int Jt Conf Artif Intell186–191. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-042

  • Erdmann E (2011) Strengths and drawbacks of voting methods for political elections. D umn edu

  • Esangbedo MO, Bai S (2019) Grey regulatory focus theory weighting method for the multi-criteria decision-making problem in evaluating university reputation. Symmetry (Basel). https://doi.org/10.3390/sym11020230

    Article  Google Scholar 

  • Esfandyari Darabad F, Mostafazadeh R, Shahmoradi R, Nasiri Khiavi A (2020) The effect of dam construction on flood and low flow indices in South of Lake Urmia. J Nat Environ Hazards 9:1–14

    Google Scholar 

  • Foli Fiagbomeh R, Bürger-Arndt R (2015) Prioritization of strategies for protected area management with local people using the hybrid SWOT-AHP analysis: the case of Kakum conservation area, Ghana. Manag Sci Lett 5:457–470. https://doi.org/10.5267/j.msl.2015.3.008

    Article  Google Scholar 

  • Gajbhiye S, Mishra SK, Pandey A (2013) Prioritization of shakkar river catchment through morphometric analysis using remote sensing and gis techniques. J Emerg Technol Mech Sci Eng 4:129–142

    Google Scholar 

  • Gallego-Ayala J, Juízo D (2011) Strategic implementation of integrated water resources management in Mozambique: an A’WOT analysis. Phys Chem Earth 36:1103–1111. https://doi.org/10.1016/j.pce.2011.07.040

    Article  Google Scholar 

  • Gehrlein WV, Valognes F (2001) Condorcet effciency: a preference for indifference. 193–205

  • Ghaleno MRD, Meshram SG, Alvandi E (2020) Pragmatic approach for prioritization of flood and sedimentation hazard potential of watersheds. Soft Comput 24:15701–15714. https://doi.org/10.1007/s00500-020-04899-4

    Article  Google Scholar 

  • Ghasemlounia R, Utlu M (2021) Flood prioritization of basins based on geomorphometric properties using principal component analysis, morphometric analysis and Redvan’s priority methods: a case study of Harşit River basin. J Hydrol 603:127061. https://doi.org/10.1016/j.jhydrol.2021.127061

    Article  Google Scholar 

  • Haibo M, Xin D, Wenjuan C (2018) Application of synthetic unit hydrograph on HEC-HMS model for flood forecasting. In: MATEC web of conferences, p1076

  • Halwatura D, Najim MMM (2013) Application of the HEC-HMS model for runoff simulation in a tropical catchment. Environ Model Softw 46:155–162

    Google Scholar 

  • Hammad M, Van Leeuwen B, Mucsi L (2018) Land cover change investigation in the southern Syrian coastal basins during the past 30-years using Landsat remote sensing data. J Environ Geogr 11:45–51

    Google Scholar 

  • Hasanloo M, Pahlavani P, Bigdeli B (2019) Flood risk zonation using a multi-criteria spatial group fuzzy-ahp decision making and fuzzy overlay analysis. Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch 42:455–460. https://doi.org/10.5194/isprs-archives-XLII-4-W18-455-2019

    Article  Google Scholar 

  • Hema hc, Govindaiah S, Lakshmi S, Surendra HJ (2021) Prioritization of sub-watersheds of the Kanakapura watershed in the Arkavathi river basin, Karnataka, India- using remote sensing and GIS. Geol Ecol Landsc 5:149–160. https://doi.org/10.1080/24749508.2020.1846841

    Article  Google Scholar 

  • Hou J, Zhou N, Chen G et al (2021) Rapid forecasting of urban flood inundation using multiple machine learning models. Nat Hazards 108:2335–2356

    Google Scholar 

  • Huctanu E, Mihu-Pintilie A, Urzica A et al (2020) Using 1D HEC-RAS modeling and LiDAR data to improve flood hazard maps accuracy: a case study from Jijia Floodplain (NE Romania). Water 12:1624

    Google Scholar 

  • Hui R, Lund JR, Madani K (2016) Game theory and risk-based leveed river system planning with noncooperation. Water Resour Res 52:119–134

    Google Scholar 

  • Ibrahim-Bathis K, Ahmed SA (2016) Rainfall-runoff modelling of Doddahalla watershed—an application of HEC-HMS and SCN-CN in ungauged agricultural watershed. Arab J Geosci 9:1–16

    CAS  Google Scholar 

  • Janssen S, Hermans L (2017) Assessment of nature-based flood defence implementation potential—development and application of game theory based method. 1–26

  • Janssen J, Krol MS, Schielen RMJ et al (2010) Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models. Ecol Modell 221:1245–1251

    Google Scholar 

  • Janssen S, Vreugdenhil H, Hermans L, Slinger J (2020) On the nature based flood defence dilemma and its resolution: a game theory based analysis. Sci Total Environ 705:135359. https://doi.org/10.1016/j.scitotenv.2019.135359

    Article  CAS  Google Scholar 

  • Jhariya DC, Kumar T, Pandey HK (2020) Watershed prioritization based on soil and water hazard model using remote sensing, geographical information system and multi-criteria decision analysis approach. Geocarto Int 35:188–208

    Google Scholar 

  • Jin H, Liang R, Wang Y, Tumula P (2015) Flood-runoff in semi-arid and sub-humid regions, a case study: a simulation of Jianghe watershed in Northern China. Water 7:5155–5172

    Google Scholar 

  • Jothibasu A, Anbazhagan S (2016) Flood susceptibility appraisal in Ponnaiyar River Basin, India using frequency ratio (FR) and Shannon’s entropy (SE) models. Int J Adv Rem Sens GIS 5:1946–1962

    Google Scholar 

  • Kang Y, Hou J, Tong Y, Shi B (2021) A hydrodynamic-based robust numerical model for debris hazard and risk assessment. Sustainability 13:7955

    Google Scholar 

  • Khiavi AN, Vafakhah M, Sadeghi SH (2022) Comparative prioritization of sub-watersheds based on flood generation potential using physical, hydrological and co-managerial approaches. Water Resour Manag 36:1897–1917

    Google Scholar 

  • Kruse R, Schwecke E, Heinsohn J (2012) Uncertainty and vagueness in knowledge based systems: numerical methods. Springer, New York

    Google Scholar 

  • Kumar D, Dhaloiya A, Nain AS et al (2021) Prioritization of watershed using remote sensing and geographic information system. Sustain 13:1–22. https://doi.org/10.3390/su13169456

    Article  Google Scholar 

  • Kuntiyawichai K (2014) Effectiveness of Ubol Ratana and Lam Pao reservoirs for flood mitigation in the downstream area of the Chi River basin using HEC-HMS Model. In: Advanced materials research. pp 785–790

  • Levy JK (2005) Multiple criteria decision making and decision support systems for flood risk management. Stoch Environ Res Risk Assess 19:438–447

    Google Scholar 

  • Li Z, Jia X, Jin H et al (2021) Determining optimal municipal solid waste management scenario based on best-worst method. J Environ Eng Landsc Manag 29:150–161. https://doi.org/10.3846/jeelm.2021.14843

    Article  CAS  Google Scholar 

  • Li P, Wang D, Li W, Liu L (2022) Sustainable water resources development and management in large river basins: an introduction. Environ Earth Sci 81:1–11. https://doi.org/10.1007/s12665-022-10298-9

    Article  Google Scholar 

  • Liang TC, Peng SH (2017) Using analytic hierarchy process to examine the success factors of autonomous landscape development in rural communities. Sustain. https://doi.org/10.3390/su9050729

    Article  Google Scholar 

  • Lin L, Li M, Chen H et al (2020) Integrating landscape planning and stream quality management in mountainous watersheds: a targeted ecological planning approach for the characteristic landscapes. Ecol Indic 117:106557

    Google Scholar 

  • Liu PCY, Lo HW, Liou JJH (2020) A combination of DEMATEL and BWM-based ANP methods for exploring the green building rating system in Taiwan. Sustain 12:3216. https://doi.org/10.3390/SU12083216

    Article  Google Scholar 

  • Machac J, Hartmann T, Jilkova J (2018) Negotiating land for flood risk management: upstream-downstream in the light of economic game theory. J Flood Risk Manag 11:66–75

    Google Scholar 

  • Madani K (2010) Game theory and water resources. J Hydrol 381:225–238

    Google Scholar 

  • Mahjouri N, Bizhani-Manzar M (2013) Waste load allocation in rivers using fallback bargaining. Water Resour Manag 27:2125–2136

    Google Scholar 

  • Majumder P, Balas VE, Paul A, Baidya D (2021a) Application of improved fuzzy best worst analytic hierarchy process on renewable energy. PeerJ Comput Sci 7:1–27. https://doi.org/10.7717/peerj-cs.453

    Article  Google Scholar 

  • Majumder R, Warier RR, Ghose D (2021b) Game-theoretic model based resource allocation during floods. arXiv Prepr arXiv211201439

  • Manavi SM, Shahedi K, Habib Nejad M, Ghermezcheshmeh B (2022) Flood generation potential and flood producing area determination using ArcGIS software and ModClark model in Talar watershed. Irrig Water Eng 12:470–486

    Google Scholar 

  • Mardookhpour A, Ooshaksaraie L (2015) Evaluating the effect of deforestation on the runoff-peak by KINFIL model (case study: Sepidroud catchment). J Environ Sci Technol 17:210–220

    Google Scholar 

  • Marhaento H, Booij MJ, Hoekstra AY (2018) Hydrological response to future land-use change and climate change in a tropical catchment. Hydrol Sci J 63:1368–1385

    Google Scholar 

  • Mendoza GA, Martins H (2006) Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. For Ecol Manag 230:1–22

    Google Scholar 

  • Merz B, Aerts J, Arnbjerg-Nielsen K et al (2014) Floods and climate: emerging perspectives for flood risk assessment and management. Nat Hazards Earth Syst Sci 14:1921–1942

    Google Scholar 

  • Meshram SG (2021) Application of fuzzy best worse. Multi criteria decision making method for flood prioritization

  • Meyer V, Scheuer S, Haase D (2009) A multicriteria approach for flood risk mapping exemplified at the Mulde river. Ger Nat hazards 48:17–39

    Google Scholar 

  • Mishra D, Satapathy S (2020) MCDM approach for mitigation of flooding risks in Odisha (India) based on information retrieval. Int J Cogn Inform Nat Intell 14:77–91. https://doi.org/10.4018/IJCINI.2020040105

    Article  Google Scholar 

  • Moosakhaani M, Salimi L, Sadatipour MT et al (2021) Game theoretic approach for flood risk management considering a financial model. Environ Eng Res 27:210368–210360. https://doi.org/10.4491/eer.2021.368

    Article  Google Scholar 

  • Moslem S, Alkharabsheh A, Ismael K, Duleba S (2020) An integrated decision support model for evaluating public transport quality. Appl Sci 10:1–19. https://doi.org/10.3390/APP10124158

    Article  Google Scholar 

  • Najafi E, Karimi M (2020) Flood risk evaluation and zoning using with AHP-fuzzy combined model with emphasis on urban safety (case study: region 1 of Tehran municipality).13–18

  • Nasiri Khiavi A, Mostafazadeh R, Esmali A et al (2019a) Changes in environmental flow components under the effect of Sabalan dam in the Qarehsou River of Ardebil Province. J Watershed Manag Res 10:85–94

    Google Scholar 

  • Nasiri Khiavi A, Mostafazadeh R, Esmali Ouri A et al (2019b) Alteration of hydrologic flow indicators in Ardabil Balikhlouchai River under combined effects of change in climatic variables and Yamchi Dam construction using range of variability approach. Watershed Eng Manag 11:851–865

    Google Scholar 

  • Nasiri Khiavi A, Vafakhah M, Sadeghi SH (2021) The impressibility of flood regime from rainfall and land use changes in Cheshmeh Kileh Watershed. Iran J Ecohydrol 8:221–234

    Google Scholar 

  • Nasiri Khiavi A, Vafakhah M, Sadeghi SHR (2023) Application of participatory approach in identifying critical sub-watersheds based on flood generation potential in The Cheshmeh-Kileh Watershed, Mazandaran Province. Water Soil Manag Model

  • Natarajan S, Radhakrishnan N (2020) An integrated hydrologic and hydraulic flood modeling study for a medium-sized ungauged urban catchment area: a case study of Tiruchirappalli City using HEC-HMS and HEC-RAS. J Inst Eng Ser A 101:381–398

    Google Scholar 

  • Noori N, Kalin L, Sen S et al (2016) Identifying areas sensitive to land use/land cover change for downstream flooding in a coastal Alabama watershed. Reg Environ Chang 16:1833–1845

    Google Scholar 

  • Pamučar D, Ecer F, Cirovic G, Arlasheedi MA (2020) Application of improved best worst method (BWM) in real-world problems. Mathematics. https://doi.org/10.3390/MATH8081342

    Article  Google Scholar 

  • Parsian S, Amani M, Moghimi A et al (2021) Flood hazard mapping using fuzzy logic, analytical hierarchy process, and multi-source geospatial datasets. Remote Sens. https://doi.org/10.3390/rs13234761

    Article  Google Scholar 

  • Peng SH (2019) Landscape assessment for stream regulation works in a watershed using the analytic network process (ANP). Sustainability. https://doi.org/10.3390/su11061540

    Article  Google Scholar 

  • Pokhrel Y, Burbano M, Roush J et al (2018) A review of the integrated effects of changing climate, land use, and dams on Mekong river hydrology. Water 10:266

    Google Scholar 

  • Popescu I, Jonoski A, Van Andel SJ et al (2010) Integrated modelling for flood risk mitigation in Romania: case study of the Timis–Bega river basin. Int J River Basin Manag 8:269–280

    Google Scholar 

  • Prinos P (2009) Review of flood hazard mapping. T03-07-01

  • Qi W, Ma C, Xu H et al (2022) A comprehensive analysis method of spatial prioritization for urban flood management based on source tracking. Ecol Indic 135:108565. https://doi.org/10.1016/j.ecolind.2022.108565

    Article  Google Scholar 

  • Qin Q, Tang H, Chen H (2011) Zoning of highway flood-triggering environment for highway in Fuling District, Chongqing. In: 2011 international conference on photonics, 3D-imaging, and visualization, p 820530

  • Rahaman SA, Ajeez SA, Aruchamy S, Jegankumar R (2015) Prioritization of sub watershed based on morphometric characteristics using fuzzy analytical hierarchy process and geographical information system—a study of Kallar Watershed, Tamil Nadu. Aquat Procedia 4:1322–1330. https://doi.org/10.1016/j.aqpro.2015.02.172

    Article  Google Scholar 

  • Rahman KU, Balkhair KS, Almazroui M, Masood A (2017) Sub-catchments flow losses computation using Muskingum–Cunge routing method and HEC-HMS GIS based techniques, case study of Wadi Al-Lith, Saudi Arabia. Model Earth Syst Environ 3:1–9. https://doi.org/10.1007/s40808-017-0268-1

    Article  Google Scholar 

  • Rahmati O, Samadi M, Shahabi H et al (2019) SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors. Geosci Front 10:2167–2175. https://doi.org/10.1016/j.gsf.2019.03.009

    Article  Google Scholar 

  • Rahmoun T, Zhao W, Hammad M, Hassan M (2018) Ruralization vs. urbanization sprawl as guiding regional planning: development scenario for rivers watershed in the southern syrian coastal region. In: IOP conference series: earth and environmental science. p 12033

  • Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega (United Kingdom) 64:126–130. https://doi.org/10.1016/j.omega.2015.12.001

    Article  Google Scholar 

  • Rezaei M, Vafakhah M, Ghermezcheshmeh B (2017) others Spatial variability of flood source areas using “unit flood response” method. Eur Water 43–48

  • Romanescu G, Constantin Stoleriu C (2017) Exceptional floods in the Prut basin, Romania, in the context of heavy rains in the summer of 2010. Nat Hazards Earth Syst Sci 17:381–396. https://doi.org/10.5194/nhess-17-381-2017

    Article  Google Scholar 

  • Saaty TL (1980) The analytical hierarchy process, planning, priority. Resour Alloc RWS Publ USA

  • Saaty TL (1996) Decision making with dependence and feedback: the analytic network process. RWS publications, Pittsburgh

    Google Scholar 

  • Saaty TL (1999) Basic theory of the analytic hierarchy process: how to make a decision. Rev la Real Acad Ciencias Exactas Fis y Nat 93:395–423

    Google Scholar 

  • Seejata K, Yodying A, Wongthadam T et al (2018) Assessment of flood hazard areas using analytical hierarchy process over the lower Yom Basin, Sukhothai Province. Procedia Eng 212:340–347. https://doi.org/10.1016/j.proeng.2018.01.044

    Article  Google Scholar 

  • Sheikhmohammady M, Kilgour DM, Hipel KW (2010) Modeling the Caspian sea negotiations. Group Decis Negot 19:149–168

    Google Scholar 

  • Siekelova A, Podhorska I, Imppola JJ (2021) Analytic hierarchy process in multiple–criteria decision–making: a model example. SHS Web Conf 90:01019. https://doi.org/10.1051/shsconf/20219001019

    Article  Google Scholar 

  • Sinha R, Bapalu GV, Singh LK, Rath B (2008) Flood risk analysis in the Kosi river basin, North Bihar using multi-parametric approach of analytical hierarchy process (AHP). J Indian Soc Remote Sens 36:335–349. https://doi.org/10.1007/s12524-008-0034-y

    Article  Google Scholar 

  • Skardi MJE, Afshar A, Sandoval-solis S (2013) Simulation-optimization model for non-point source pollution management in watersheds: application of cooperative game theory. KSCE J Civ Eng 17:1232–1240. https://doi.org/10.1007/s12205-013-0077-7

    Article  Google Scholar 

  • Strapazan C, Petru\ct M (2017) Application of arc hydro and hec-hms model techniques for runoff simulation in the headwater areas of covasna watershed (romania). Geogr Tech 12

  • Tabarzadi A, Jourgholami M, Moghaddam Nia A et al (2019) Evaluation of the effect of forest cover on quantitative and qualitative runoff parameters in Chitgar Forest Park Watershed, Tehran. J Range Watershed Manag 71:997–1011

    Google Scholar 

  • Taherdoost H (2020) Decision making using the analytic hierarchy process (AHP); a step by step approach. Int J Econ Manag Syst 2:244–246

    Google Scholar 

  • Tassew BG, Belete MA, Miegel K (2019) Application of HEC-HMS model for flow simulation in the lake tana basin: the case of gilgel abay catchment, upper blue nile basin. Ethiop Hydrol 6:21

    Google Scholar 

  • Te Chow V, Maidment DR, Mays LW (1962) Applied hydrology. J Eng Educ 308:1959

  • Toosi SLR, Samani JMV (2017) Prioritizing watersheds using a novel hybrid decision model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR. Water Resour Manag 31:2853–2867

    Google Scholar 

  • Üçler N, Engin GO, Köçken HG, Öncel MS (2015) Game theory and fuzzy programming approaches for bi-objective optimization of reservoir watershed management: a case study in Namazgah reservoir. Environ Sci Pollut Res 22:6546–6558

    Google Scholar 

  • Vafakhah M, Fakher Nikche A, Sadeghi SH (2018) Comparative effectiveness of different infiltration models in estimation of watershed flood hydrograph. Paddy Water Environ 16:411–424. https://doi.org/10.1007/s10333-018-0635-1

    Article  Google Scholar 

  • Viezzer J, Schmidt MAR, dos Reis ARN et al (2022) Restoration of urban forests to reduce flood susceptibility: a starting point. Int J Disaster Risk Reduct 74:102944

    Google Scholar 

  • Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water (Switzerland). https://doi.org/10.3390/w11020364

    Article  Google Scholar 

  • Vreugdenhil H, Janssen S, Hermans L, Slinger J (2022) Cooperating for added value: using participatory game theory in implementing nature-based flood defences. Ecol Eng 176:106507. https://doi.org/10.1016/j.ecoleng.2021.106507

    Article  Google Scholar 

  • Waiyasusri K, Chotpantarat S (2020) Watershed prioritization of Kaeng Lawa sub-watershed, Khon Kaen province using the morphometric and land-use analysis: a case study of heavy flooding caused by tropical storm podul. Water (Switzerland). https://doi.org/10.3390/W12061570

    Article  Google Scholar 

  • Wang Y, Li Z, Tang Z, Zeng G (2011) A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting Lake Region, Hunan, Central China. Water Resour Manag 25:3465–3484

    Google Scholar 

  • Wang J, Stern MA, King VM et al (2020) PFHydro: a new watershed-scale model for post-fire runoff simulation. Environ Model Softw 123:104555. https://doi.org/10.1016/j.envsoft.2019.104555

    Article  Google Scholar 

  • Wolfslehner B, Vacik H (2008) Evaluating sustainable forest management strategies with the analytic network process in a pressure-state-response framework. J Environ Manag 88:1–10

    Google Scholar 

  • Wu T (2021) Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis. Ecol Indic 129:108006

    Google Scholar 

  • Wu Y, Zhang B, Xu C, Li L (2018) Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective. Sustain cities Soc 40:454–470

    Google Scholar 

  • Wubalem A, Tesfaw G, Dawit Z et al (2021) Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: in a case study of the Lake Tana sub-basin in northwestern Ethiopia. Open Geosci 13:1668–1688. https://doi.org/10.1515/geo-2020-0329

    Article  Google Scholar 

  • Xu Q, Chen J, Peart MR et al (2018) Exploration of severities of rainfall and runoff extremes in ungauged catchments: a case study of Lai Chi Wo in Hong Kong, China. Sci Total Environ 634:640–649

    CAS  Google Scholar 

  • Yang L, Feng Q, Yin Z et al (2017) Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China. Hydrol Process 31:1100–1112

    CAS  Google Scholar 

  • Yariyan P, Avand M, Abbaspour RA et al (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat Nat Hazards Risk 11:2282–2314

    Google Scholar 

  • Yazdi J, Golian S, Roohi M (2017) Determining checkdams layout for flood mitigation using simulation–optimization approach. Int J Environ Res 11:395–413. https://doi.org/10.1007/s41742-017-0036-0

    Article  Google Scholar 

  • Yusop Z, Chan CH, Katimon A (2007) Runoff characteristics and application of HEC-HMS for modelling stormflow hydrograph in an oil palm catchment. Water Sci Technol 56:41–48

    CAS  Google Scholar 

  • Zhang S, Wang J, Xu Z (2019) The application of HEC-HMS in mountain torrents simulation of Northern small watershed. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/252/5/052060

    Article  Google Scholar 

Download references

Funding

This research with Project No. 99023697 was supported by Iran National Science Foundation (INSF).

Author information

Authors and Affiliations

Authors

Contributions

ANK: Conceptualization, Methodology, Software, Writing; MV: Supervision, Writing–original draft, Validation; SHS: Conceptualization, Writing–original draft, Validation.

Corresponding author

Correspondence to Mehdi Vafakhah.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nasiri Khiavi, A., Vafakhah, M. & Sadeghi, S.H. Flood-based critical sub-watershed mapping: comparative application of multi-criteria decision making methods and hydrological modeling approach. Stoch Environ Res Risk Assess 37, 2757–2775 (2023). https://doi.org/10.1007/s00477-023-02417-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-023-02417-0

Keywords:

Navigation