Influence of river cross-section data resolution on flood inundation modeling: Case study of Kashkan river basin in western Iran

Journal Pre-proofs Research papers Influence of River Cross-Section Data Resolution on Flood Inundation Mod‐ eling: Case Study of Kashkan River Basin in Western Iran Fatemeh Geravand, Seiyed Mossa Hosseini, Behzad Ataie-Ashtiani PII: DOI: Reference:

S0022-1694(20)30203-1 https://doi.org/10.1016/j.jhydrol.2020.124743 HYDROL 124743

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

10 December 2019 18 February 2020 19 February 2020

Please cite this article as: Geravand, F., Hosseini, S.M., Ataie-Ashtiani, B., Influence of River Cross-Section Data Resolution on Flood Inundation Modeling: Case Study of Kashkan River Basin in Western Iran, Journal of Hydrology (2020), doi: https://doi.org/10.1016/j.jhydrol.2020.124743

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© 2020 Published by Elsevier B.V.

Influence of River Cross-Section Data Resolution on Flood Inundation Modeling: Case Study of Kashkan River Basin in Western Iran

Fatemeh Geravanda, Seiyed Mossa Hosseinia,1, Behzad Ataie-Ashtianib,c a Physical b Department c National

Geography Department, University of Tehran, P.O. Box 14155-6465, Tehran, Iran.

of Civil Engineering, Sharif University of Technology, P.O. Box 11155-9313, Tehran, Iran

Centre for Groundwater Research & Training, College of Science & Engineering, Flinders University, GPO Box 2100, Adelaide, South Australia 5001, Australia.

Abstract In this study, a coupling of a hydrologic and hydraulic model was utilized to assess the impacts of river geometry data resolution on the flood inundation characteristics in a data-scarce environment. Hydrological modeling incorporates soil conservation service curve-number (SCS-CN) and the geomorphologic based instantaneous unit hydrograph model (GIUH) to compute the direct runoff hydrograph in Kashkan river basin located in western Iran. 1D HEC-Geo-RAS model was used and performed to simulate inundation extent of 100-𝑦𝑟 floods (~1800 𝑚3/𝑠) along 40 𝑘𝑚 reach of Kashkan river with a ground survey of river cross-section (2,000 cross-sections, each including 500 data-points). The effect of cross-section spacing (20 𝑚 to 5,000 𝑚) and dataset resolution along cross-section (5 to 500 points) on 100-𝑦𝑟 (~1800 𝑚3/𝑠) flood inundation extent area (𝐹𝐼𝐴), average flow depth (𝐹𝐷), and velocity (𝐹𝑉) in river centerline were scrutinized. Results indicate that coarser cross-section spacing (20 𝑚 to 5,000 𝑚) showed an overestimating tendency to simulate 𝐹𝐼𝐴 (~5.75%), 𝐹𝐷 (391.34%), and 𝐹𝑉 (31.2%). While increasing grid resolution (5 to 500 points) on cross-section tends to underestimate of 𝐹𝐼𝐴 (17.84%), 𝐹𝐷 (84.13%), and 𝐹𝑉 (49.12%). The predominant effect of cross-section spacing on 𝐹𝐼𝐴, 𝐹𝐷, and 𝐹𝑉 was found

1

Corresponding Author. Email address: [email protected] (S.M. Hosseini).

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compared to the number of data-points on a cross-section. Results of this study could potentially be considered as a baseline for mapping of flood inundation extent in the environment that ground or remotely sensed high-resolution topographic data of river cross-section are scarce while making a trade-off between model accuracy, computational time, and cost of data compilation is crucial. Key-words: Flood Inundation, River Basin Modeling, Data-Scarce Environment, Cross-Section Data resolution, HEC-Geo-RAS

1. Introduction Floods are among the most devastating natural disasters in the world (Zhang et al., 2014). Global climate change and anthropogenic impacts caused severe flood occurrences by enhancing frequency, intensity, uncertainties and pattern of precipitation (IPCC, 2014). During 2000 to 2015, flooding is responsible for almost half of all weather-related disasters and affected 2.3 billion people (CRED, 2015), 20.4% of death rate, and 19.3% of damages (~40% of all-natural disasters), culminating to $US 70.1 billion worldwide (Fustos et al., 2017). The US set a record in 2017 with $300 billion in disaster losses from hurricanes and flooding (NOAA, 2018). The mapping of flood hazard areas is the basis for providing fundamental information on flood risk mitigation strategies (Degiorgis et al., 2012) and helps in relocation process (Mind'je et al., 2019). Generally, three groups of approaches on inundation modeling are the subject of researches since 1970s, including empirical methods, hydrodynamic models, and conceptual (rapid inundation) models as shown schematically in Fig. 1. A technical review on flood inundation models, classification, and their advantages and limitations are reviewed in-depth by Teng et al. (2017) and Afshari et al. (2018). Despite the advancements in the development of inundation models (as listed in Fig. 1), study for the estimation of flood hazard mapping in developing countries is avoidable, especially due to lack of high-resolution topography data (i.e. LiDAR) 2

(Jamali et al., 2018; Tanaka et al., 2019). In such regions, flood is becoming more disastrous due to high vulnerabilities, weak infrastructure, poor population mindset, low level of resilience and lack of strong and sustainable mitigation measures (Hong et al., 2018). Computational time and cost of the ground surveying or remotely sensed of river geometry and cross-section data point make them unaffordable (El-Sayed, 2018). Accuracy of the hydraulic model to delineate flood inundation mainly depends on the accuracy of the geometric data of river cross-section, grid resolution of the served DEM to define the floodplain, and peak-flow data as boundary conditions (Papaioannou et al., 2016). These factors should be more critical in developing countries where financial sources for ground data compilation using surveying are limited (Pinos and Timbe, 2019). High-resolution DEM data (i.e. LiDAR) is very expensive in some countries (e.g. Iran) or is not available (Rathjens et al., 2016). In the U.S., LiDAR data are available only for less than 40% of the continental states (Snyder, 2012). In absent of highresolution data especially for identifying river cross-section geometry, the ground surveyed elevation datasets is the only alternative that imposes high costs for data compilation (Prakash et al., 2014). Federal Emergency Management Agency (FEMA, 2016) estimated that flood inundation mapping could cost from $3000 to $6000/𝑘𝑚 of river reach in the U.S. In the case of a limited budget, scarcity and sparsity of high-resolution topographic data, and time of data processing, the challenge is minimizing the river geometry dataset required to a reliable production for flood inundation mapping on large-scale (Samela et al., 2017). Although there is a plethora of researches as reported previously, focused on delineation flood inundation areas by hydrodynamic models (1-D or 2-D models), surprisingly a few studies have been considered the effect of data resolution of river cross-section on model efficiency. In terms of topographical data, hydraulic modelling requires a certain number of cross-sections and data-points on them to simulate

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acceptable results (Fewtrell et al., 2011). Nevertheless, only a few guidelines are available to assist the modeller in determining the location or spacing between the cross-sections (e.g. Castellarin et al., 2009). Samuels (1990) emphasized to include the following locations of river cross-sections may produce better simulation by hydrodynamic models: at both ends of the river reach, at upstream/downstream of every structure across the river (i.e. bridge, culvert, and weir), at all discharge and water level stations along the reach. However, having cross-section data at finer spacing or at every river meander (i.e. river bend) and at upstream/downstream of structures (e.g. bridges) will increase the cost of the topographical surveys. Despite this general outline, there are still unanswered research questions on the effect of subjectivity of cross-section location along the river through on-ground surveying. This study comes to bridge the gap identified in the literature related to the nexus between hydraulic modeling results (e.g. area extents, depths, and flow velocities) and resolution of river cross-section data. Accuracy and efficiency are two aims in evaluating the performance of flood inundation models, which are often in conflict with each other (Zhang et al., 2014). A trade-off is needed between the accuracy of the results, computational efficiency of hydraulic model, and costs associated with the on-ground data surveying for mapping the flood extent in the data-scarce environment. Moreover, the hydrodynamic models were executed for the watersheds that have sufficient recorded peak flow data, while little attention is made for the un-gauged watersheds pronounced in developing countries located in Asia, Africa, or South America (Santiago-Collazo et al., 2019). Here, the term of “un-gauged basin” means runoff observation in sub-daily scale to record the peak flow discharge are rare (Komi et al., 2017). In such circumstances, the attention was shifted towards the linkage of a hydrologic with a hydraulic model to generate flood inundation map (Hostache et al., 2018). This linkage increases the flexibility of the hydraulic model to capture a

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broad range of storm events where the model is needed to execute in the basin (Yu and Coulthard, 2015). Thus, this study coupled a hydrologic and hydraulic model to deepen the knowledge on processes required for transforming design rainfall into flood inundation areas. The processes considered in the developed approach including a modified soil conservation service method (SCS-CN) to estimate excess rainfall, a geomorphologic based instantaneous unit hydrograph model (GIUH) for transferring the excess rainfall to the flood hydrographs, and finally, a 1D hydrodynamic model (HEC-Geo-RAS) (Brunner, 2001) to delineate flood-prone areas. For this purpose, the hydraulic behavior of long reach (~40 𝑘𝑚) in a high mountain river above 2000 𝑚 a.s.l. located in western Iran with high point density elevation measurements through ground surveying (2000 cross-sections, each including 500 data-points) is simulated for a 100-𝑦𝑟 design flood with a magnitude of 1800 𝑚3 /𝑠. The organization of the paper is the following. In the Section 2, we described structure of the hydrological and hydraulic models briefly and completed with how the resolution of river crosssection data is considered for flood inundation modeling, and quantified indicators used for evaluation of hydraulic model efficiency. In Section 3, we described the study area and dataset considered to test the developed model. Estimation of peak flow discharge by hydrologic model, and hydraulic modeling of flood inundation in the study river are given in Section 4.

2. Materials and Methods A schematic diagram of the developed model in this study is illustrated in Fig. 2. To accomplish the objectives listed in the Introduction section, the methodology includes SCS-CN to estimate excess-rainfall, GIUH to estimate direct runoff hydrograph (DRH), and HEC-Geo-RAS for flood modeling and inundation mapping. They were coupled in a one-way framework and executed over 5

a large distance of a river (40 𝑘𝑚) including dense spaced cross-section topographic dataset for floods with return periods of 10- to 500-𝑦𝑟. A brief description of three considered models is given in the next sections.

2.1. GIUH Model The GIUH theory developed by Rodriguez-Iturbe and Valdes (1979) assumes that the watershed can be represented as a linear, time-invariant system, such that the total surface runoff (𝑄𝑡 ′ ) can be obtained based on the convolution integral of instantaneous unit hydrograph (IUH) ordinates and excess rainfall (𝐸𝑅𝑡 ) (Hosseini et al., 2016): 𝑡′

𝑗

𝑄𝑡 ′ = ∑ 𝐸𝑅𝑡 × ∑ ∑ 𝐾𝑖𝑗 × 𝑃(𝑤𝑠 ) × 𝑒 −𝑇𝑥𝑖 ×𝑡 ⏟ 𝑠∈𝑆 𝑖=2 𝑡=𝑡 ′ −𝑚

(1)

𝐼𝑈𝐻 (𝑡)

where 𝑡 ′ is time step. 𝑇𝑥𝑖 represents the inverse of mean holding time of the component 𝑥𝑖 including both overland (𝑥𝑜𝑖 ) and channel flows (𝑥𝑖 ) in 𝑚𝑖𝑛 and considered as the parameter of the exponential probability density function. 𝑃(𝑤𝑠 ) is the path probability of the surface flow path space (𝑊𝑠 ) which includes {𝑥𝑜𝑖 ‚ 𝑥𝑖 ‚ 𝑥𝑗 ‚ … ‚𝑥Ω }; 𝐾𝑖𝑗 are coefficients related to mean holding time of 𝑥𝑜𝑖 and 𝑥𝑖 as follows (Kumar and Kumar, 2008): 𝐾𝑖𝑗 =

𝑇𝑥1 𝑇𝑥2 … 𝑇𝑥𝑗 (𝑇𝑥1 − 𝑇𝑥𝑖 )(𝑇𝑥2 − 𝑇𝑥𝑖 ) … (𝑇𝑥𝑖−1 − 𝑇𝑥𝑖 )(𝑇𝑥𝑖+1 − 𝑇𝑥𝑖 ) … (𝑇𝑥𝑗 − 𝑇𝑥𝑖 )

(2)

Here, the following expression of 𝑇𝑥𝑖 developed by Gupta et al. (1980) based on combination of Manning’s equation and kinematic-wave approximation is used:

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0.6

1 1 1 1 = + , 𝑇𝑥𝑖 𝑡𝑥𝑜 𝑡𝑥𝑖 𝑖

𝑡𝑥𝑜

𝑖

6.978 𝑛 𝐿𝑜𝑖 = 0.4 ( ) 𝐼 √𝑆𝑜𝑖

𝑃𝑜𝐴 𝐴Ω −1/3 1 1 = × [ 𝑖 2] 2 𝑁𝑖 𝐿̅𝑖 {𝑡𝑥𝑖 𝑡𝑥𝑜𝑖

,

1<𝑖<Ω

(3)

where 1/𝑡𝑥𝑜 and 1/𝑡𝑥𝑖 are mean travel time of 𝑖 th overland-flow, 𝐼 is average rainfall intensity, 𝑖

𝐿𝑜𝑖 is length of overland flow path, 𝑛 is Manning’s roughness coefficient (Welle and Woodward, 1986) and ranges from 0.10 (for smooth surfaces) to 0.8 (for dense woods region) according to Table 3-1 of TR-55 (1986 printing), and 𝑆𝑜𝑖 is average overland slope. 𝑃𝑜𝐴 is the ratio of the 𝑖

surface flow region of the 𝑖th order hillslope to the total watershed area (𝐴Ω ). 𝑁𝑖 is the number of 𝑖th order. 𝐿̅𝑖 is average length of 𝑖th order. Inverse dependency of 1/𝑇𝑥𝑖 to 𝐼 in Eq. 3 reveals that travel time of flow in each component 𝑥𝑖 decreases with increasing the rainfall intensity for lowfrequent (or high return period) events. As shown in Fig. 2, the inputs of GIUH model are geomorphologic characteristics of the studied basin and the corresponding output are the IUH ordinates.

2.2. SCS-CN model The SCS-CN model (USDA SCS, 1985) calculates the excess rainfall (𝐸𝑅𝑡 ) according to the following equation: (𝑃𝑡 − 𝐼𝑎 )2 𝐸𝑅 = 𝑓𝑜𝑟 𝑃𝑡 > 𝐼𝑎 { 𝑡 𝑃𝑡 − 𝐼𝑎 + 𝑆 𝐸𝑅𝑡 = 0 𝑓𝑜𝑟 𝑃𝑡 ≤ 𝐼𝑎

(4)

where 𝑃𝑡 is rainfall depth (𝑚𝑚), 𝐼𝑎 is initial abstraction (𝑚𝑚), 𝑆 is the potential maximum retention of watershed (𝑚𝑚) which is expressed in terms of the dimensionless curve number (𝐶𝑁) which represents the runoff potential of the land cover-soil complex characteristics governed by

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soil antecedent moisture condition (AMC), soil type, and land use and treatment and formulated through the following relationship (USDA SCS, 1985):

𝑆=

25400 − 254 𝐶𝑁

(5)

𝐼𝑎 = 𝜆 × 𝑆

(6)

where 𝜆 are initial abstraction ratio. The final expression of the SCS-CN method is given as 2 25400 [ 𝑃𝑡 − 𝜆 ( 𝐶𝑁 − 254)] 𝐸𝑅𝑡 = 25400 𝑃𝑡 + (1 − 𝜆 ) ( 𝐶𝑁 − 254)

(7)

2.3. Hydraulic model The simplest representation of floodplain flow is to treat the flow as 1D along the centerline of the river, and channel geometry as a property of each node on the river line and solve equations derived by ensuring mass and momentum conservation (i.e. 1D Saint-Venant equations) between two cross-sections (Brunner, 2016). Most numerical hydraulic models traditionally used in practical river engineering are 1D models, which compared to higher dimensional models are simpler to use, requiring minimal input data and computation. The drawbacks associated with the 1D hydrodynamic models are, inability to simulate lateral diffusion of the flood wave, the discretisation of topography as cross-sections rather than as a continuous surface, the subjectivity of cross-section location and orientation, and incapability to simulate the timing and duration of flood extent (Teng et al., 2017). However, modeling the interaction of floodplain and channel flows in a two-dimensional (2D) representation provides greater realism and accuracy of results (Balica et al., 2013). Accurate execution of the hydraulic models depends on the substantial spatial details (e.g. channel and floodplain cross-sections, optimum parameter values), which are often 8

not readily available. Aronica et al. (2012) found that the 1D/2D-hybrid model LISFLOOD-FP showed a lower sensitivity to the floodplain Manning coefficient than towards the channel Manning coefficient. Lamichhane and Sharma (2018) reported that Manning’s roughness of the channel section was found to be more sensitive than that of the floodplain. While Horritt and Bates (2002) came to a similar conclusion for this model, they found a higher sensitivity to floodplain roughness values for the finite element model TELEMAC-2D and the 1D model HEC-RAS. Based on a global sensitivity analysis performed by Pappenberger et al. (2008), channel roughness (Manning’s coefficient, 𝑛) is the most important parameter of HEC-RAS 1D model in flood inundation estimation. The public domain 1-D simulation model of HEC-Geo-RAS version 10.2 developed by Hydrologic Engineering Center (HEC) of the US Army Corps of Engineers Hydrologic Engineering Center (USACE, 2000) is used and performed in steady-state condition to delineate the flood inundation areas around of the study river. Among the models listed in Fig. 1, the 1D HEC-RAS has been the principal model used in US Federal Emergency Management Agency (FEMA)’s National Flood Insurance Program (Brown, 2016) and National Oceanic and Atmospheric Administration (NOAA)’s Advanced Hydrologic Prediction Service (McEnery et al., 2005). In steady-state condition, HEC-Geo-RAS computes water surface elevation (WSE) and velocity at discrete cross-sections by solving continuity, energy and flow roughness (Manning) equation with an iterative procedure called the standard step method (Brunner, 1995). Thus, for a given upstream flow and downstream boundary condition, the output from HEC-RAS is areaaveraged WSE and velocity at each cross-section. Data requirements for HEC-Geo-RAS include topographic information of a series of cross-sections, Manning’s 𝑛 values across each crosssection (both in the channel bed and floodplain), and flow data including peak flow rates, flow

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change locations (e.g. bridges, culverts, meanders), and boundary conditions. Modeling using the HEC-Geo-RAS includes three main steps (Fig. 2) (USACE, 2010): I) HEC-Geo-RAS pre-processing, ii) HEC-RAS modeling, and iii) HEC-Geo-RAS postprocessing. For pre-processing of data, 3D Analyst tool was used to convert digitized contours into Triangulated Irregular Network (TIN). Interpolation was done to convert vector data (TIN) into raster data (DEM). Moreover, DEM of study area was used as main input in order to create RAS layers like stream centerline, bank lines, bank stations, flow path centerlines. Stream lines, bank stations, flow paths, levees, brides, cross-sections and ineffective flow areas were digitized in HEC-Geo-RAS to convert to the channel geometry required by HEC-RAS. Due to the sensitivity of water surface profiles to cross-sections intervals (Haile, 2005), cross-sections were digitized at intervals less than 20 𝑚 and perpendicular to streamlines to provide an accurate estimation of the river channel. Finally, all these geometric data were encoded with elevation values from the raster surface and a GIS import file for HEC-RAS was created. II) HEC-RAS modeling: Generally, HEC-RAS is the most common hydraulics program to model floodplain (Wurbs and James, 2002) and it is accepted by many government agencies and private organizations. To run HEC-RAS model, topographic information of cross-sections, flow rates and flow change locations, flow condition (sub-critical, super-critical or mixed-flow), boundary conditions and manning’s n values for each cross-section are required. Peak flows for different design storms from GIUH models were applied to the hydraulic model to generate the water surface elevation, velocity, and flow regime at each cross-section, and also flood extent for the study reach. The last required data for HEC-RAS is Manning’s n values for each cross-section (for both channel and floodplain) which was determined according to land use data and reference Tables (e.g. Chow et al., 1988).

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III) HEC-Geo-RAS post-processing: by running HEC-RAS model based on geometrical data from HEC-Geo-RAS and the maximum peak flow data generated by GIUH, water elevation and water velocity were calculated in each cross-section and then exported again to HEC-Geo-RAS to create flood inundation and velocity maps. Water elevation data for each scenario were converted to TIN. Then, the grid surface was created based on TIN surface and was subtracted from the terrain. Velocity data were exported to Geo-RAS in point format, each point representing estimated velocity at each cross-section slice. These were also interpolated to a TIN, and then vertices converted to a GRID form and overlaid onto their respective terrain surfaces for careful checking of validity.

2.4. Resolution of Cross-Section Geometric Data The methodology of investigating cross-section data resolution as the main objectives of this study involves changing the geometric description to reproduce new flood inundation maps by HECGeo-RAS model is schematically represented in Fig. 3. Geometric data description includes: i) geometric mesh resolution (GMR) denotes to space between two successive cross-sections (𝐷𝑐𝑠 ) and number of cross-sections along the study reach (𝑁𝑐𝑠 ), and ii) geometric mesh node (GMN) denotes to the number of elevation data-points (here named node) along each cross-section (𝑁𝑛𝑜𝑑𝑒 ) and average distance between the nodes (𝐷𝑛𝑜𝑑𝑒 ) as shown in Fig. 3. To assess the effect of GMR in flood inundation by HEC-Geo-RAS through the study reach (~40 𝑘𝑚), 8 cross-section configurations with resolution (𝐷𝑐𝑠 ) of 20 𝑚 to 5,000 𝑚 (𝑁𝑐𝑠 =2,000 to 8) were used. Seven Configurations of GMNs along the cross-sections with mesh size (𝐷𝑛𝑜𝑑𝑒 ) of 0.5 𝑚 to 100 𝑚 (𝑁𝑛𝑜𝑑𝑒 =500 to 5) were considered to investigate the effect of this geometric description on HECGeo-RAS model. The inundation results simulated by the HEC-Geo-RAS model with considering

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the original cross-sectional configuration including 𝑁𝑐𝑠 = 2,000, 𝐷𝑐𝑠 = 20 𝑚, 𝑁𝑛𝑜𝑑𝑒 = 500, 𝐷𝑛𝑜𝑑𝑒 = 0.5 𝑚 based on ground surveyed collected by the regional water authority of Lorestan Province in 2015 were taken as baseline in order to investigate the effects of topographic data resolution on generated inundation maps (also named alternative). The resolutions of surveyed data of river and floodplain (𝑁𝑛𝑜𝑑𝑒 = 500, 𝑁𝑐𝑠 = 2,000) are fine enough to identify abrupt topographical variability along and across the river. All configurations of GMRs/GMNs were prepared manually by the exclusion of alternate cross-sections/nodes in ArcGIS to obtain a certain space between two sequential cross-sections/nodes. The idea behind this task is to assess the subjectivity that arises from placing cross-sections, and the number of nodes in each cross-section during data compilation, and how this affects the produced flood inundation map through the hydraulic model. Generally, the finer mesh resolutions are inefficient from a computational standpoint since the process of a numerical model to produce the results required for generating flood inundation map are time-consuming. Totally, 15 geometric files including 8 cross-section configurations of GMR and 7 configurations of GMN were prepared and executed for 6 design floods (10 to 500- 𝑦𝑟) in the study reach. Thus, 90 HEC-Geo-RAS models were created and simulated. For all these simulations, the Manning’s roughness and flow change locations (e.g. bridges, meanders) that had a significant effect on the model result were retained in all configurations and just upstream or downstream cross-sections of these structures were excluded. Typically, the manning coefficient for floodplains is higher than the main channel because of vegetation and rougher surface. In all simulations, ArcGIS extension version 10.1 was used for pre-processing of input information (creating geometry data required for the hydraulic model as TIN layer), and for post-processing of model output (creating inundation maps). One-dimensional HEC-Geo-RAS version 4.1.1 (Ackerman, 2005) in steady-state flow

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condition uses Manning’s equation to generate streamflow-stage rating curves along the studied reach. Using these rating curves, input peak flow corresponding to a given return period was converted into a stage height. Finally, the raster was used to create the inundation extents at these stage heights. The boundary conditions of the hydraulic model were prescribed using data from the hydrometric gauge at Doab-Veysian hydrometric station (stage-derived discharge). Quantifying the differences in inundation extents and depths between the different configurations and the reference (baseline) were performed by the following indicators: 1) Fit percentage indicator, 𝐹𝐼 (%): measures agreement between results of two configurations (baseline and alternative) in predicating flood extent areas (Lhomme et al., 2008): 𝐹𝐼 =

𝐵 × 100 𝐵+𝐶+𝐷

(8)

where 𝐵 represents the number of pixels (or area) inundated in both models, 𝐶 is the number of pixels (or area) inundated in baseline but dry in alternative, and 𝐷 is the number of pixels (or area) inundated in alternative but dry in baseline. A fit value closer to 100% represents a better agreement in flood extent prediction. 2) Bias percentage indicator, 𝐵𝐼 (%): represents the relative percentage error in estimating of the final extent of the flooded area for baseline and alternative configuration (Bernini and Franchini, 2013): 𝐵+𝐶 𝐵𝐼 = ( − 1) × 100 𝐵+𝐷

(9)

Positive value of 𝐵𝐼 indicates overestimation of the extent flooded area compared to the base value, whereas negative values indicate underestimation. Ideal value of 𝐵𝐼 is zero. The criteria of FI and BI were also adopted to evaluate the effect of cross-section configurations (both modifications of

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GMN and GMR) on final inundation maps simulated by HEC-Geo-RAS model through for the study reach.

3. Study Area The developed hydro-inundation model was tested for Kashkan river basin with an area of about 3,825 𝑘𝑚2 located in western Iran within the Zagros Simply fold belt (Fig. 4). The Kashkan basin is characterized by hilly and mountainous relief with a minimum elevation of about 1,067 𝑚 and its high elevation found in the northeast region (>3,500 𝑚) above sea level (Fig. 4). Numerous intermittent and ephemeral streams discharge to the Kashkan River only during of intense rainfall periods and (or) heavy snowmelt (Shahriari Nia et al., 2015). The partitioning of the basin in parts (sub-basins) allows a proper treatment of the topology and geometry of the basins, introspect of the original idea behind the used models (e.g. GIUH), which can be exploited as soon as data are available to identify a model’s parameters at sub-scales (Rigon et al., 2016). The study basin was divided into six study areas (five sub-basins and one whole basin) according to the location of existing hydrometric stations as the outlet of study areas. Automatic delineation of basin and sub-basins boundaries (Fig. 4) were conducted using a DEM of 12.5 𝑚 obtained from the Shuttle Radar Topography Mission (SRTM) provided by National Aeronautics and Space Administration (NASA) (http://www.dwtkns.com). The basic climatologic, geomorphologic, and geological characteristics of the study areas are summarized in Table 1. The climate in the Kashkan basin is predominantly Cold Mountain. The mean annual precipitation ranges from 400 (in the central regions) to 900 𝑚𝑚/𝑦𝑟 (in the northeast mountainous parts, upstream of Dare-Tang sub-basin) in the form of snow (with winter being the wettest season). In

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this area, middle winter is the wettest period and July to September are the driest. While the mean annual flow in the study basin are in the range of 2.38 𝑚3 /𝑠 in Dare-Tang sub-basin to 33.04 𝑚3 /𝑠 in Doab-Veisian sub-basin (values of 𝑄̅ in Table 1); the specific discharge (calculated as 𝑄̅ /𝐴) varies between 4.46 𝑙𝑖𝑡/𝑠. 𝑘𝑚2 in Doab-Veisian to 13.57 𝑙𝑖𝑡/𝑠. 𝑘𝑚2 in the Dare-Tang (values of 𝑄̅𝑠 in Table 1). Peak and low stream flows in the study areas are experienced in March and September, respectively. While the maximum precipitation occurs in January, a lag time of three months is observed between the time of maximum precipitation and peak flow because of precipitation in the high-elevation areas is snow rather than rain. The vegetation distributed above 1000 𝑚 is mainly oak forests or shrub lands, while the vegetation distributed below 1000 𝑚 is mostly grassland (Rahmati et al., 2016). Land uses in the study area (as shown in Fig. S2) are predominantly dry-farming (covers 41%), forest (covers 39%), and pasture (covers 20%). Urban development has been limited to the north and the northeast region of the basin (Fig. 4). The surface area is geologically composed mainly of shale, marl, conglomerate, limestone, and Quaternary alluvium at different ages consisting of clay, silt, sand, and gravel mixed in varied proportions (Alavi, 1994) (Fig. S1 in the Supporting Information and Table 1). Kashkan river basin has experienced harsh flash floods (with a peak flow greater than 1000 𝑚3/𝑠) during 1971-2015, the latest sever flood that affected the entire basin is related to flood of March 2019, with a peak of 3000 𝑚3/𝑠 (~ 300- 𝑦𝑟 return period) recorded in Doab-Veisian hydrometric station located at the basin outlet (Lorestan Water Regional Authority, 2018). This region was selected primarily because the Kashkan basin is a high-mountainous area that provides distinct physical (topographic and geomorphic) and climatic settings, and experienced hazardous flash floods. Therefore, providing good test beds for the study. Moreover, the high-resolution ground surveyed river cross-sections for a length of 40 𝑘𝑚 (A-A' reach in Fig. 4) is available to assess the 15

effect of cross-section data resolution on flood inundation through developed model. The GIS data for geometric description of study reach includes 2,000 ground surveyed river cross-sections (𝑁𝑐𝑠 ) with 20 𝑚 average distance (𝐷𝑐𝑠 ). The average width of these cross-sections is 500 𝑚 (including elevation data of bed and floodplain) with an average spacing of 0.5 𝑚 along each crosssection (𝐷𝑛𝑜𝑑𝑒 ). These data collected by the regional water authority of Lorestan Province in 2015. The floodplain over the study reach (segment A-A' in Fig. 4) is semi-rural, with many fields being used for pasture or having been ploughed for dry-farming. There are several major roads crossing the study reach and a few hydraulic control structures on the main river including two bridges located in the 4.20 and 4.80 𝑘𝑚 from the basin outlet. According to a previous study (Hosseini et al., 2018) the appropriate values of 𝜆 and 𝐶𝑁 for the studied sub-basins are in the range of 0.21 (Dehno) to 0.22 (Pole-Kashkan), and 38.20 (Dehno) to 60.60 (Dare-Tang), respectively as also shown in Table S1 in the SI. The peak flows of simulated hydrographs (with 𝑇=10 to 500 𝑦𝑟) by GIUH were compared to the ones obtained by the frequency analysis (FA) through fitting the appropriate probability distribution to the annual peak flow series recorded at the corresponding gaging stations. The simulated peak flows by GIUH with a return period of 100- 𝑦𝑟 were used as the input of the hydraulic model as one of the scenario of flow condition generated from design storms. In this study, a user-friendly FORTRAN code was developed to accomplish the GIUH computations. This code can generate IUH and DRH based on geomorphologic characteristics that the user enters in an Excel file.

4. Results and Discussion

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According to the flowchart of the methodology adopted in this study (Fig. 2), the obtained results are presented and discussed in the following categories: characteristics of design storms, SCS-CN estimation of excess-rainfall depths, estimation of IUHs by GIUH model in brief description, and hydraulic modeling of flood inundation by HEC-Geo-RAS in detail.

4.1. Characteristics of design storms in the study areas The IDF curve of study basin was obtained based on analysis of 128 storm events recorded in oneminute interval by a rain gauge located in the center of basin (outlet of Kaka-Reza sub-basin in Fig. 4). The derived IDF curve for the study basin is shown in Fig. S3 (in the supporting information). Durations of design storms for six study areas were obtained based on the average time of concentration (𝑡𝑐 ) of each area (Chow et al., 1988). A preliminary estimation of 𝑡𝑐 for each study area was conducted using the empirical relationships of Kirpich (1940), Kerby-Hathaway (Kerby, 1959), and SCS lag time (McCuen and Okunola, 2002). Considering the duration of design storm equal to 𝑡𝑐 assures to produce maximum peak flow for a specific rainfall (Kumar, 2015). Knowing the design storm durations, the characteristics of design storms with return periods of 2to 500- 𝑦𝑟 were extracted from the IDF curves for each study areas. The values of design storm depths (𝑅𝐷 ) obtained for the study areas are shown in Table 2.

4.2. SCS-CN estimation of excess-rainfall depths Using the SCS-CN formula (Eq. 7), the excess rainfall (𝐸𝑅𝐷 ) correspond to the depths of design storms (𝑅𝐷 values in Table 2) in each study area were computed while the appropriate values of 𝐶𝑁 and 𝜆 were considered (Table S1). The computed values of 𝐸𝑅𝐷 ranged 1.28 𝑚𝑚 to 8.98 𝑚𝑚 as shown in Table 2.

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4.3. Estimation of peak flow discharge by GIUH model The 𝐸𝑅𝐷 values with 𝑇=2 to 500-𝑦𝑟 computed by the SCS-CN method for the six study areas were considered as one of GIUH model input to estimate the DRH. For estimation of the mean holding time of flow through different overland regions and channels (Eq. 3), the intensity of design storms was obtained from the IDF curve (Fig. S3). Manning’s roughness coefficient was estimated from the land use map of study areas and the reference tables for Manning’s 𝑛 values by Chow et al., (1988). The computed values of 1/𝑡𝑥𝑜 and 1/𝑡𝑥𝑖 and the trend of mean holding time of study areas 𝑖

for the design storms of 2- to 500-𝑦𝑟 are shown in Table S1 and Fig. S4. The IUHs derived from the GIUH model (Eq. 1) for six study areas are shown in Fig. S5. Using the Eq. 1, the ordinates of DRH with 𝑇=2 to 500 𝑦𝑟 for the study areas were computed. Accuracy of the GIUH model in peak flow estimation with certain return period (i.e. 2 to 500 𝑦𝑟) was evaluated by those obtained from the frequency analysis of annual peak flow series recorded in each sub-basin outlet by fitting the Generalized Extreme Value (GEV) distribution. Base-flows have been estimated at a constant rate for each study areas and added to estimate direct runoff hydrograph. The results of the GIUH model to estimate peak flow rates of flood hydrographs and in the study areas were compared to those obtained from frequency analysis (Table 3). Good agreement were found between the simulated peak flow rates and frequency analysis based on the percentage of errors (𝑃𝐸𝑝 ). Here, the sources of error is the accumulative effect of multiple approaches incorporated to estimate peak flows (i.e. IDF curves, SCS-CN method, GIUH model, and frequency analysis). DEM resolution to delineate the basin boundaries and extracting the initial geomorphologic parameters of study areas may be another source of error (Komi et al., 2017). Two raster grids of SRTM for the study areas in the resolutions of 10 𝑚 and 30 𝑚 were used in order to assess the sensitivity of the estimated IUHs by GIUH model to the DEM resolution. As 18

shown in Table S2, DEM resolutions (10 m and 30 m) affect insignificantly the geomorphological properties (number and length of streams, and upstream area) of the streams with order 1 and 2. Using a coarser DEM (i.e. 30 𝑚) leads to an underestimation of geomorphological characteristics of streams orders 1 and 2. No significant difference was observed between the IUHs derived by the GIUH model when DEM with resolutions of 10 𝑚 and 30 𝑚 was used. The peak flow values (sum of DRH peak flow and baseflow) estimated by the GIUH model were considered as one of the inputs of the hydraulic model as discussed in the next section.

4.4. Hydraulic modeling of Flood Inundation As described formerly, to run HEC-Geo-RAS 1D model, topographical information of crosssections, flow rates and flow change locations, flow condition, boundary conditions and manning’s 𝑛 values should be provided for each cross-section (including main river and floodplain) of study reach (A-A' in Fig. 4). Most of the studies on the hydraulic modeling of flood inundation extend have implemented with steady flow condition (Cook and Merwade, 2009). Thus, in this study to find the inundated areas, hydraulic model was run in steady-state super-critical flow condition, and the upstream boundary conditions was a known discharge (i.e. peak flow rate) and free surface flow. Based on the field investigation of study reach and land use map of the floodplain, average Manning’s 𝑛 coefficient of channel and floodplain was considered 0.01 and 0.06, respectively from reference tables (Chow, 1959). As mentioned before, the HEC-Geo-RAS model with 15 different cross-section resolutions including 8 configurations with 𝐷𝑐𝑠 = 20 𝑚 to 5,000 𝑚 (𝑁𝑐𝑠 =2,000 to 8), and 7 configurations with 𝑁𝑛𝑜𝑑𝑒 =5 to 500 (𝐷𝑛𝑜𝑑𝑒 = 0.5 𝑚 to 100 𝑚) were built for simulating the flood inundation extent for the study reach. The cross-section configuration with 𝐷𝑐𝑠 = 20 𝑚 (𝑁𝑐𝑠 =2,000) and 𝑁𝑛𝑜𝑑𝑒 =500 (𝐷𝑛𝑜𝑑𝑒 = 0.5 𝑚) was considered as reference or baseline 19

to assess other configurations. To enable an even assessment, apart from the cross‐section configurations, the input forcing data to the hydraulic model (i.e. input streamflow, topographic resolution, and the values of channel/surface roughness) were kept identical when they were compared across different configurations. Moreover, the cross-sections at the strategic locations (around two bridges) were reserved across all configurations. Flood inundation extent map simulated by the HEC-Geo-RAS considering reference cross-section configuration and peak flow rates of 10 to 500 𝑦𝑟 are shown in Fig. 5 (a-f). Variations of water depth (𝑊𝐷) in river and floodplain is also shown in these figures. The 𝑊𝐷 values ranged 0.79 𝑚 (for 𝑇= 10 𝑦𝑟) to 7.83 𝑚 (for 𝑇= 500 𝑦𝑟). The values of flood inundation area (𝐹𝐼𝐴) suggest expansion of inundation extents for the study reach in the range of 12.8 𝑘𝑚2 (for 𝑇=10 𝑦𝑟) to 33.1 𝑘𝑚2 (for 𝑇=500 𝑦𝑟). In flood prone regions, agriculture areas of 6.0 to 25.5 𝑘𝑚2 and semi-rural residential areas of 2.0 to 23.7 𝑘𝑚2 inundated along the study reach when flood 2 to 500 𝑦𝑟 occurs. In addition, the hydraulic model performance (e.g. 𝐹𝐼𝐴) was not sensitive to Manning’s 𝑛 especially for low-frequent floods (>100 𝑦𝑟) when the optimal 𝑛 was changed to ±30%. This behavior is commonly seen (e.g. Komi et al., 2017) during large flood events, by increasing water depth inundation area tends to extent and is not affected by surface roughness.

4.5. Effect of Cross-Section Data Resolution on Flood Inundation Inter-comparison of simulated inundation extent area, flow velocity, and water depth were carried out for all 15 cross-section configurations with the reference model based on five criteria as follows: i) a fit percentage indicator (𝐹𝐼) and bias percentage indicator (𝐵𝐼) for inundation extent area, ii) cost of ground survey of cross-section data-points and average percentage of error (𝐴𝑃𝐸) for depth and velocity of flow in river centerline, and iii) flood inundation area (FIA). Fig. 6 20

compares the 𝐹𝐼 and 𝐵𝐼 values for model simulated inundation with different cross-section configurations extend for 100 𝑦𝑟 flood event (~1800 𝑚3/𝑠). Since, HEC-Geo-RAS model for other flood events indicates similar behavior, the corresponding values of 𝐹𝐼 and 𝐵𝐼 are not shown here. The 𝐹𝐼 value decreases from 85.4% to 53.8% when cross-section spacing (𝐷𝑐𝑠 ) increases from 40 𝑚 to 5,000 𝑚 (Fig. 6-a). This indicates if cross-section space doubled, the efficiency of hydraulic model in flood inundation extent, decreases 0.5%. While the cost of surveying varies between 3,200 USD to 400,000 USD (based on the fiscal year of 2018). According to the 𝐵𝐼 values, two different behaviors could be observed for resolution of cross-section spacing: an underestimation in 𝐹𝐼𝐴 for 𝐷𝑐𝑠 <100 𝑚 (negative values of 𝐵𝐼) and overestimation of flood extent area for 𝐷𝑐𝑠 >100 𝑚 (positive values of 𝐵𝐼) when it compared with the base value (𝐷𝑐𝑠 =20 𝑚). The coarser resolution of elevation data-points (or nodes) along each cross-section (i.e. decreasing of 𝑁𝑛𝑜𝑑𝑒 from 400 to 5 nodes) would produce overestimation flood inundation extent (positive values of 𝐵𝐼 in Fig. 6b). While the coarser resolution of data-points in cross-sections (i.e. smaller value of 𝑁𝑛𝑜𝑑𝑒 ) resulted in lower agreement between the simulated inundation area with the base values (𝐹𝐼 decreases), and the associated cost of data compilation through surveying reduced from 640,000 USD to 8,000 USD. Between two topographic attributes of river cross-section (𝐷𝑐𝑠 and 𝑁𝑛𝑜𝑑𝑒 ) the resolution of cross-section nodes (𝑁𝑛𝑜𝑑𝑒 ) is a dominant factor in the prediction of inundation extent areas. Interestingly, the effects of coarser cross-section spacing and cross-section mesh node on 𝐹𝐼𝐴 are reversed (Fig. 7); the first increases (as 17.84% when 𝐷𝑐𝑠 =40 to 5,000 𝑚), and the later decreases (as 5.65% when 𝑁𝑛𝑜𝑑𝑒 =500 to 5). Overall, if 𝐷𝑐𝑠 doubled, the 𝐹𝐼𝐴 value will increase as 0.05%; while halving 𝑁𝑛𝑜𝑑𝑒 , will decrease 𝐹𝐼𝐴 as 0.35%. Compared to other researches, Saksena and Merwade (2015) and Lamichhane and Sharma (2018) also reported that the 𝐹𝐼𝐴 simulated by 1D HEC-RAS increased as the coarser resolution of datasets derived from SRTM 21

and LiDAR was used. Cook and Merwade (2009) developed 1D hydraulic modelling for two selected river reaches in USA (North Carolina and Texas) and highlighted that the simulated flood inundation extent becomes larger as the number of cross‐section decreases. In addition to flood extent area, the variables of interest for most end-users of hydraulic models are the hydraulic characteristics of flow (especially water level and flow velocity) at different places of river and floodplain (Hunter et al., 2005). For this purpose, the average percentage of errors in depth (𝐴𝑃𝐸𝑦 ) and velocity (𝐴𝑃𝐸𝑣 ) of flow in river centerline were also compared across different cross-section configurations. Figure 8 shows the 𝐴𝑃𝐸𝑦 and 𝐴𝑃𝐸𝑣 values for different resolution of cross-section spacing (𝐷𝑐𝑠 =40 to 5,000 𝑚) and flow rates (𝑇=10 to 500 𝑦𝑟). For all flow rates, the values of 𝐴𝑃𝐸𝑦 reduce when the finer resolution of cross-section space is used. An interesting part is that a dramatic increase of 𝐴𝑃𝐸𝑦 was observed when the distance between the cross-sections increases from 1,000 𝑚 (63.5%) to 5,000 𝑚 (455.6%) in order of ~7 (Fig. 8). Moreover, the coarser resolution of cross-section space produces overestimation of water depth in hydraulic simulations (Fig. 8). If 𝐷𝑐𝑠 doubled, water depth and velocity would increase as 3.13% and 0.25%, respectively. This may be due to omitting the effect of flow change locations (e.g. river meanders and bends) on flow by interpolating the geometry between two successive cross-sections that is enough coarse. In other words, increasing cross-section spacing caused lack of topographical information available during the computation stage by RAS to generate accurate water surface profiles (Castellarin et al., 2009). For a given cross-section space, the more error in water depth was averagely related to high-frequent floods (e.g. 𝑇=10 𝑦𝑟) which may because of roughness of river especially floodplain areas on smaller depths of flow. Generally, an increase in cross-section space, resulted in water depth increasing, therefore, increased the inundation extent. This finding is consistent with the Cook and Melwade (2009) that reported reducing the number 22

of cross-sections result in smaller inundation maps, but the results tended to be contradictory with Md Ali et al. (2015). They investigated the effect of cross-section spacing of 1000 𝑚 (~41 crosssections) to 8000 𝑚 (~6 cross-sections) in flood inundation of Sungai Johor Basin, in Malaysia using 1D HEC-RAS model. Their results indicated that the error of water depth does not change significantly, as the spacing between the cross‐sections increased from 1,000 𝑚 to 8,000 𝑚. The effect of cross-section spacing on the flow velocity in river centerline simulated by the hydraulic model in term of 𝐴𝑃𝐸𝑣 was investigated and shown in Fig. 8-b. The effect of crosssection data resolution (cross-section spacing and the number of data-points along cross-sections) on simulated flow velocity by the hydraulic model has not been reported previously. Like 𝐴𝑃𝐸𝑦 , the 𝐴𝑃𝐸𝑣 value averagely increases (31.2% or 1.5 𝑚/𝑠) as cross-sections more spaced (up to 5,000 𝑚). Decreasing resolution of topographic data-points (𝑁𝑛𝑜𝑑𝑒 =500 to 5) lead to underestimation of average flow velocity in river centerline (49.12% or 2.3 𝑚/𝑠) (Fig. 9-a). This arises from any changes in 𝑁𝑛𝑜𝑑𝑒 would affect the shape of the cross-section, longitudinal characteristics of the river (e.g. slope), and alter the flow change locations (e.g. meanders and bends) generated by the RAS, and all affected the flow velocity. This may the reason why the variations of 𝐴𝑃𝐸𝑣 due to cross-section spacing does not follow a general rule as shown in Fig. 9-b. For clarification, the effect of 𝑁𝑛𝑜𝑑𝑒 along cross-section on the geometry of river and floodplain, generated shapes of a cross-section located in distance of 4.30 𝑘𝑚 from the end of study reach by HEC-RAS model for 𝑁𝑛𝑜𝑑𝑒 = 5 to 5,000 are shown in Fig. 10. This behavior inconsistent with the effect of crosssection spacing as the higher error in water depth simulation was related to higher frequency flows (the maximum error was 94.0% observed for flow with 𝑇=10 𝑦𝑟 when 𝑁𝑛𝑜𝑑𝑒 = 5). The hydraulic model with considering configuration 𝑁𝑛𝑜𝑑𝑒 = 5 shows the poorest performance for all return periods with significant and systematic underestimation of depth, velocity, FIA, and flood extent 23

for the entire river reach. Also, the effect of cross-section resolution (i.e. 𝐷𝑐𝑠 and 𝑁𝑛𝑜𝑑𝑒 ) on depth and velocity of flow does not follow a regular pattern for different flow rates. The maximum effect of geometric mesh node (i.e. 𝑁𝑛𝑜𝑑𝑒 ) on the water depth and velocity was obtained for 10-𝑦𝑟 flow. While, the geometric mesh resolution (i.e. 𝐷𝑐𝑠 ) has the highest impacts on the water depth and velocity for flow condition of 𝑇=10 𝑦𝑟 and 50 𝑦𝑟, respectively. The minimum effect of crosssection resolution on water depth and velocity are not related to a certain flow rate. The results also reveal the predominant effect of cross-section data-point (𝑁𝑛𝑜𝑑𝑒 ) on 𝐹𝐼𝐴, and flow hydraulic (depth and velocity) compared to the cross-section spacing (𝐷𝑐𝑠 ).

5. Conclusion Accurate floodplain extent maps in the basins with scarcity and sparsity of high-resolution crosssection topographic data are crucial tools for the managers and insurance actuaries to make appropriate decisions to plan rescue operation during flooding periods. In this study, a coupling of hydrologic (SCS-CN and GIUH) and hydraulic (1D HEC-Geo-RAS) models were used to simulate flood inundation mapping for different flow rates (10- to 500-𝑦𝑟). This study scrutinized and quantified the effects of cross-section data resolution for 40 𝑘𝑚 reach along Kashkan River including densely spaced ground survey cross-sections in the simulation of flood inundation extent. The following conclusions could be drawn from this study:  For given flow conditions, the geometric node resolution (i.e. the number of data-points along cross-section) is predominant in describing the inundation extent and flow characteristics (e.g. depth and velocity) with 1D hydraulic model. For example, if cross-section spacing (𝐷𝑐𝑠 ) doubled, the 𝐹𝐼𝐴 value will increase as 0.05%; while halving 𝑁𝑛𝑜𝑑𝑒 , will decrease 𝐹𝐼𝐴 as 0.35%. 24

 A coarser resolution in both cross-sections spacing and mesh nodes would decrease the efficiency of hydraulic model in flood inundation extent in terms of 𝐹𝐼 and 𝐵𝐼. Even with the availability of high-resolution topographic data from satellites (e.g. LIDAR), the level at which these data were captured in the geometric description of river (e.g., the spacing of cross-section or grid size) could have a significant impact on the model prediction.  Generally, increasing cross-section spacing (𝐷𝑐𝑠 ), resulted in increasing of 𝐹𝐼𝐴, flow depth and velocity. While decreasing cross-section mesh nodes, will result in decreasing of 𝐹𝐼𝐴, flow depth and velocity. Therefore, it will be better to generate flood inundation maps based on a slightly higher cross-section space and dense value of elevation dataset so that the affected areas, flow depth and velocity overestimated. Hence, slightly overpredicted result in inundation extent might be helpful while planning and making flood warning decisions and to minimize the adverse consequences of such hazards. When the inundation extent estimated through the coarse resolution of elevation data and cross-section space, some factor of safety should be considered. In addition, reducing the details of topographic dataset to hydraulic model (i.e. lower resolution of cross-section) speeds-up simulation time, reduce the computational cost, and usually comes at the manageable degree of flood inundation accuracy.  Coupling a hydrological model (e.g. GIUH) with the 1D hydraulic model increases the flexibility of the hydraulic model to capture a broad range of storm events wherever the model is needed to execute in the basin.  However, the quantitative results of this study are perhaps specific to this basin and to the models adopted. The results of this study offer a deep perception of the interplay among the geometric description of river cross-section through 1D hydraulic modeling in the final inundation mapping in topographic data-sparse regions. 25

 In this study, a steady-state condition of flow was adopted for hydraulic modeling to simulate final flood inundation state. The unsteady flow simulation is essential for the flood routing, time evolution of flood extent and timely evacuation of people from probable inundation area. This study provides a baseline for improving hydraulic modeling results and mapping of flood inundation extents in areas where only coarser resolution cross-section dataset are available. By replicating the methodology presented here for the regions with different topographic and climate conditions, relationships between cross-section properties and flood inundation characteristics

could be developed.

Supporting Information More results of the hydrologic models (SCS-CN and GIUH) have been provided in the SI.

Acknowledgements Authors would like to acknowledge Lorestan Water Regional Authority for the support in preparing the data. The work was supported by Iran National Science Foundation (INSF) under grant number 97005777.

6. References Ackerman, C.T. (2005). HEC-Geo-RAS: GIS Tools for Support of HEC-RAS Using ArcGIS. Users Manual Version 4. US Army Corps of Engineers. Afshari, S., Tavakoly, A.A., Rajib, M. A., Zheng, X., Follum, M.L., Omranian, E., & Fekete, B.M. (2018). Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model. Journal of hydrology, 556, 539-556. Alavi, M., (1994). Tectonics of the Zagros orogenic belt of Iran; new data and interpretations. Tectonophysics 229, 211–238. 26

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31

Fatemeh Geravand: Data Collection, Executing the Software, and Preparing Maps. Seiyed Mossa Hosseini: Analysis of the Results, Visualization, and Writing- Original draft preparation. Behzad Ataie-Ashtiani: Writing- Reviewing and Editing of the Manuscript.

32

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

33

Fig. 1. Classification of models developed recently for flood inundation. These models are reviewed by the Teng et al. (2017), Afshari et al. (2018), and Santiago-Collazo et al. (2019).

34

Fig. 2. Schematic methodology of the hydro-inundation model developed in this study to delineate flood inundation characteristics (i.e. extent, depth, and velocity).

35

Fig. 3. Schematic representation of geometric cross-section data resolution considered in this study: cross-section spacing with an average distance of 𝐷𝑐𝑠 (also named geometric mesh resolution, GMR), and number of nodes in each cross-section (𝑁𝑛𝑜𝑑𝑒 ) with an average horizontal distance of 𝐷𝑛𝑜𝑑𝑒 (also named geometric mesh node, GMN).

36

3

4 A

2 5 A’

1

6 A

1 Dehno

2 Kaka-Reza

3 Dare-Tang

A' Study Reach for hydraulic modeling

4 Sarab-Seidali

37

5 Pole-Kashkan

6 Doab-Veisian

Fig. 4. Location of study areas considered in this study to test developed hydro-inundation model. Section A-A' is the study reach including high-resolution ground survey cross-section data for hydraulic inundation modeling. Numbers in the figure refer to study areas cited below of figure.

WD (m)

WD (m)

a) 𝑻= 10 𝒚𝒓

b) 𝑻= 25 𝒚𝒓

(FIA=12.81 𝒌𝒎2)

(FIA=15.90 𝒌𝒎2)

WD (m)

WD (m)

c) 𝑻= 50 𝒚𝒓

d) 𝑻= 100 𝒚𝒓 2

(FIA=20.94 𝒌𝒎 )

(FIA=24.77 𝒌𝒎2)

38

WD (m)

WD (m)

f) 𝑻= 500 𝒚𝒓

e) 𝑻= 200 𝒚𝒓

(FIA=33.10 𝒌𝒎2)

(FIA=27.90 𝒌𝒎2)

Fig. 5. Flood inundation maps along the study reach (with length of 40 𝑘𝑚) simulated by HEC-Geo-RAS model with considering peak flow of 10 to 500 𝑦𝑟 and baseline cross-section resolution. Variations of water depth (WD) and flood inundation area (FIA) are also shown.

39

Fig. 6. Values of fit percentage indicator (𝐹𝐼) and bias percentage indicator (𝐵𝐼) and cost of ground survey of crosssection data for different configurations of cross-section data resolution when flow rate of 100 𝑦𝑟 is considered as upstream flow condition: (a) the effect of cross-section spacing (𝐷𝑐𝑠 =40 to 5,000 𝑚, for all cases 𝑁𝑛𝑜𝑑𝑒 = 400), and (b) the effect of number of data-points in each cross-section (𝑁𝑛𝑜𝑑𝑒 =5 to 500, for all cases 𝐷𝑐𝑠 =20 𝑚). The corresponding costs are calculated based on fiscal year of 2018.

(a)

40

Fig. 7. The effects of cross-section mesh node (number of data-points along cross-section, 𝑁𝑛𝑜𝑑𝑒 ) and cross-section spacing (𝐷𝑐𝑠 ) on flood inundation area (𝐹𝐼𝐴).

41

707.9 530.4

800

415.5

APEY (%)

700

382.7

332.4

500

97.2 400 300 200 100 0

53.2

81.5 55.9

54.3 34.1

51.5

30.9 7.5

4.2

2.4

0.7

2.2

37.7

45 24.8 27.4

40 20.1

30 25 20 15 10 5 0

43.9 25.8

44.8

37.5

35

52.6 33.2

5000 1000 8.5 0.6 500 5.2 3.2 4.3 3.5 7.1 1.4 100 4.0 0.9 3.5 1.7 5.5 80 3.3 2.8 2.5 1.1 60 1.4 10 25 50 40 100 200 T (yr) 500 6.5

41.9

APEV (%)

364.8

600

28.0

32.4

30.8 24.3

13.3

11.3 14.9 14.0 11.5 11.6 8.3 4.6 9.3 9.8 0.7 5000 4.9 1.0 1.3 1000 8.1 2.4 3.0 9.8 1.4 500 2.6 1.4 3.4 1.2 0.5 100 7.7 2.3 0.7 2.2 80 0.4 0.7 60 0.7 10 25 50 40 100 200 T (year) 500

Fig. 8. Average percentage of error of water depth (𝐴𝑃𝐸𝑦 ) and flow velocity (𝐴𝑃𝐸𝑣 ) with return periods of 𝑇= 10- to 500- 𝑦𝑟 simulated by the HEC-Geo-RAS model for different cross-section spacing (40 𝑚 to 5,000 𝑚). The water depth and flow velocity simulated for cross-section space of 20 𝑚 (𝐷𝑐𝑠 = 20 𝑚) and number of nodes in each crosssection of 500 (𝑁𝑛𝑜𝑑𝑒 = 500) are considered as the baseline to compute the 𝐴𝑃𝐸𝑦 and 𝐴𝑃𝐸𝑣 for other configurations.

42

94.0 85.3 82.2

85.2

100

68.1 64.4

67.8

67.3

79.6 77.6

79.5 76.5

66.1

60.1

80 52.3

APEY (%)

83.9 83.2 82.8 80.2

48.2 43.7

44.8

60 31.0

40

27.7

32.0

26.1 20.4 25.1 26.114.6

9.8

6.9

37.2

28.8

32.7

28.3 24.4

20

37.5

41.0

24.9 26.1 14.7

0 10

25

50

5 10 50 100 200 300

400

100

200

500

57.9 54.2 48.9

31.2

50

APEV (%)

48.7

46.0

46.2

44.3

41.4

60

40

15.7

16.3 6.4

20

1.5 1.2

10 1.0 0

0.9

4.6 3.0

6.2 2.6

0.0 6.4

25

50

20.1

10.2 12.6 5.7

0.7 3.7

18.2

10.6 8.8

13.7 11.7

0.4 4.7

1.3 10

31.3

27.5

23.5

14.6

30 2.9

29.3

5 10 50 100 200 300 400

100

200

500

Fig. 9. Average percentage of error of water depth (𝐴𝑃𝐸𝑦 ) and flow velocity (𝐴𝑃𝐸𝑣 ) for return periods of 𝑇=10- to 500- 𝑦𝑟 simulated by the HEC-Geo-RAS model for different geometric mesh node or number of data points in each cross-section (5 to 400 𝑚). The water depth and flow velocity simulated for cross-section space of 20 𝑚 (𝐷𝑐𝑠 = 20 𝑚) and number of nodes in each cross-section of 500 (𝑁𝑛𝑜𝑑𝑒 = 500) are considered as the baseline to compute the 𝐴𝑃𝐸𝑦 and 𝐴𝑃𝐸𝑣 for other configurations.

43

1026

1026 5

1022

Elevation (m)

Elevation (m)

1018 1014 1010 Bank Stations

1006

10

1022

1002

1018 1014 1010 Bank Stations

1006 1002

998

998 0

200

400

0

200

Station (m) 1026

1026 20

1022 1018

1018

1014

1014

1010 Bank Stations

1006

50-500

1022

Elevation (m)

Elevation (m)

400

Station (m)

1002 998

1010

Bank Stations

1006 1002 998

0

100

200

300

400

0

100

Station (m)

200

300

400

Station (m)

Fig. 10. Effect of geometric mesh node (GMN) or number of nodes (𝑁𝑛𝑜𝑑𝑒 ) across each cross-section on the shape of an instance cross-section (located in the outlet of study reach) in the HEC-Geo-RAS. No difference is observed for the cross-section shape when number of nodes (𝑁𝑛𝑜𝑑𝑒 ) is considered 50 to 500.

Table 1. Climatologic and geomorphologic characteristics of the study sub-basins.

44

Study

𝐴

𝑃̅

Area

(𝑘𝑚2)

(𝑚𝑚)

̅ (𝑚) 𝐻

𝑄̅ (𝑚 / 3

Geology (%Cover)

𝑄̅𝑠 (𝑙𝑖𝑡/

Dominant 𝐿𝑈 (%

𝑠. 𝑘𝑚2)

Cover)

13.57

High-density pasture

Limestone (95%), Alluvium

(62%)

(5%)

Moderate pasture

Shale and marl (4%), Limestone

(25%)

(59%), Alluvium (37%)

Dry farm and

Limestone (37%),

moderate pasture

Conglomerate (17%), Alluvium

(32%)

(46%)

Dry farm and

Shale and Marl (5%),

moderate pasture

Limestone (54%),

(40%)

Conglomerate (6%), Alluvium

𝑠) Dare-

175.36

508.10

2541.6

2.38

Tang Sarab-

800.24

493.82

1950.8

7.52

9.40

Seidali Dehno

Kaka-

280.15

1188.8

458.40

495.00

2154.0

1970.5

2.62

11.69

9.35

9.83

Reza

(35%) Pole-

3746.1

488.60

1672.4

16.69

4.46

Kashkan

Agriculture (40%),

Shale and Marl (29%),

forest (38%), and

Limestone (19%),

pasture (20%)

Conglomerate (26%), Alluvium (26%)

Doab-

3825.60

467.56

1096.0

33.04

8.64

Veysian

Agriculture (42%),

Shale and Marl (27%),

forest (38%), and

Limestone (17%),

pasture (20%)

Conglomerate (25%), Alluvium (31%)

̅ : mean height above mean seal level; 𝑄̅: Mean Annual Discharge; 𝑄̅𝑠 : specific discharge 𝐴: upstream area; 𝑃̅: Mean Annual Precipitation; , 𝐻 (𝑄̅/𝐴); 𝐿𝑈: land use.

45

Table 2. Values of design storm depths (𝑅𝐷 ) and corresponding excess rainfall depth (𝐸𝑅𝐷 ) estimated by SCS-CN model for return periods of 2- to 500- 𝑦𝑟 in the six study areas. Values of 𝑅𝐷 and 𝐸𝑅𝐷 are in 𝑚𝑚. Dare-Tang

Sarab-Seidali

Dehno

Kaka-Reza

Pole-Kashkan

Doab-Veisian

T (𝑦𝑟) 𝑅𝐷

𝐸𝑅𝐷

𝑅𝐷

𝐸𝑅𝐷

𝑅𝐷

𝐸𝑅𝐷

𝑅𝐷

𝐸𝑅𝐷

𝑅𝐷

𝐸𝑅𝐷

𝑅𝐷

𝐸𝑅𝐷

2

3.11

1.28

3.78

0.54

4.52

0.42

10.35

5.09

9.72

3.78

9.14

6.03

5

4.87

1.93

5.76

0.81

6.66

0.16

13.96

6.12

14.25

4.44

13.70

7.48

10

6.31

2.31

7.35

0.97

8.18

0.04

15.30

6.45

16.19

4.64

15.72

7.98

25

8.51

2.73

9.76

1.15

11.39

0.05

16.31

6.68

17.86

4.79

17.53

8.38

50

10.47

3.00

11.88

1.26

12.99

0.19

16.76

6.78

18.71

4.85

18.45

8.56

100

12.74

3.22

14.33

1.34

15.82

0.66

17.05

6.85

19.32

4.90

19.18

8.70

200

15.39

3.40

17.16

1.39

15.24

0.54

17.15

6.87

19.65

4.92

19.58

8.78

300

17.18

3.49

19.05

1.40

16.33

0.77

17.25

6.89

19.98

4.94

19.98

8.85

400

18.48

3.54

20.42

1.40

17.07

0.96

17.35

6.91

20.31

4.96

20.38

8.92

500

19.57

3.57

21.57

1.39

17.67

1.12

17.45

6.93

20.61

4.98

20.76

8.98

46

Table 3. Values of peak flows obtained from frequency analysis of recorded annual peak-flow series (𝑄𝑝 ), and corresponding peak flows (𝑄′𝑝 ) estimated by GIUH model for return periods of 2- to 500- 𝑦𝑟 in the six study areas (values of 𝑄𝑝 and 𝑄′𝑝 are in 𝑚3 /𝑠). In each case, the percentage of error (𝑃𝐸𝑝 ) in peak flow estimation is also computed. Negative and positive values refer to underestimation and overestimation of peak flow by GIUH model respect to the frequency analysis. T (year

Dare-Tang

Sarab-Seidali

Dehno

Kaka-Reza

Pole-Kashkan

Doab-Veisian

𝑄𝑝

𝑄′𝑝

𝑃𝐸𝑝

𝑄𝑝

𝑄′𝑝

𝑃𝐸𝑝

𝑄𝑝

𝑄′𝑝

𝑃𝐸𝑝

𝑄𝑝

𝑄′𝑝

𝑃𝐸𝑝

𝑄𝑝

𝑄′𝑝

𝑃𝐸𝑝

𝑄𝑝

𝑄′𝑝

𝑃𝐸𝑝

2

40. 4

37. 69

6.7 1

37. 43

45. 83

22. 44

54. 01

43. 15

20. 11

126 .9

144 .44

13. 82

269 .44

293. 58

8.9 6

263 .68

367. 26

39. 28

5

72. 77

59. 02

18. 90

64. 93

82. 27

26. 71

90. 83

70. 52

22. 36

214 .56

232 .41

8.3 2

496 .54

440. 34

11. 32

517 .74

600. 4

15. 97

10

96. 7

76. 47

20. 92

87. 77

101 .04

15. 12

113 .53

94. 46

16. 80

281 .53

314 .06

11. 55

676 .07

593. 64

12. 19

738 .76

838. 51

13. 50

25

130 .12

116 .82

10. 22

123 .25

140 .82

14. 26

140 .43

108 .6

22. 67

377 .9

414 .88

9.7 9

924 .1

863. 21

6.5 9

109 7.2

118 0.49

7.5 9

50

157 .43

126 .88

19. 41

155 .38

160 .58

3.3 5

159 .18

134 .19

15. 70

458 .98

546 .28

19. 02

112 0.2

106 5.9

4.8 5

143 5.4

151 5.23

5.5 6

100

186 .87

154 .38

17. 39

193 .17

195 .41

1.1 6

176 .83

155 .68

11. 96

548 .55

659 .15

20. 16

132 3.8

121 6.88

8.0 8

184 7.4

180 3.61

2.3 7

200

218 .17

174 .99

19. 79

237 .75

213 .94

10. 01

193 .52

140 .59

27. 35

681 .27

747 .42

9.7 1

153 5.4

140 3.26

8.6 1

235 0.8

229 6.86

2.2 9

300

239 .05

195 .59

18. 18

268 .26

233 .09

13. 11

203 .9

155 .5

23. 74

748

835 .69

11. 72

166 6.1

159 0.35

4.5 5

270 5

259 2.8

4.1 5

400

253 .2

216 .31

14. 57

290 .4

252 .24

13. 14

209 .32

159 .19

23. 95

794 .43

887 .52

11. 72

175 5.1

179 3.98

2.2 2

296 6.6

288 7.1

2.6 8

500

264 .91

236 .91

10. 57

309 .33

287 .81

6.9 6

214 .23

174 .1

18. 73

796 .01

928 .13

16. 60

182 7.6

195 2.4

6.8 3

319 2.9

307 1.24

3.8 1

)

47

48

Highlights 

1D HEC-RAS was developed to simulate flood inundation extent in data-scares areas



Inundation extent, depth and velocity of 10- to 500-yr flows were simulated



Effect of river cross-section spacing and mesh nodes resolution on flood inundation was scrutinized



Cross-section space has predominant effect on flood inundation compared to mesh nodes resolution

49