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Admas 2025

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  1. Wiley Advances in Meteorology Volume 2025, Article ID 5092932, 28 pages https://doi.org/10.1155/adme/5092932 Research Article Application of the HEC-RAS and HEC-HMS Models for Flood Risk Analysis in the Gumara River, Upper Blue Nile Basin, Ethiopia Mulie Admas, Tade Mule Asrade , and Walelgn Dilnesa Cherie Hydraulic and Water Resources Engineering Department, Debre Markos University, Debre Markos, Amhara, Ethiopia Correspondence should be addressed to Tade Mule Asrade; tade2009.mule@gmail.com Received 20 February 2025; Revised 7 July 2025; Accepted 16 July 2025 Academic Editor: Marzuki Marzuki Copyright © 2025 Mulie Admas et al. Advances in Meteorology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. In terms of socioeconomic damage, floods are one of the most destructive natural disasters. However, by conducting flood modeling prior to an event, their impacts can be mitigated and better managed. This study aims to assess flood risk areas in the Gumara River using HEC-RAS and HEC-HMS. Data on rainfall, stream flow, and spatial information were collected, including digital elevation models (DEMs), land use, and soil maps. Flood levels before and after dyke construction were examined using the HEC-RAS model. The impact of flood before construction of dyke for each return periods 2, 5, 10, 25, 50, and 100 years were 6232.64, 6262.51, 6375.04, 6495.62, 6639.55, and 6804.26ha, respectively, and after dyke construction also 5912.18, 5966.47, 6027.13, 6108.04, and 6483.49ha at the same return periods. The construction of dyke reduced impact of flooding for each return periods were 5.14%, 4.73%, 5.46%, 5.97%, 7.73%, and 4.71%, respectively. However, the dyke did not fully eliminate flood risks. Particularly during the summer months of July and August, the research area is still quite vulnerable. Based on these findings, the study recommends prioritizing the construction of a storage dam upstream of the Gumara Bridge, as it would offer more effective flood routing and protection for downstream settlements. River channelization and the addition of a silt excluder may provide supplementary benefits, particularly in maintaining flow capacity and reducing sediment buildup. Additional dykes and barriers along key tributaries could further mitigate localized flooding. However, these should be considered secondary measures following comprehensive upstream flood control through dam storage. Keywords: dykes; floods; Gumara River; hydraulic modeling; hydrological modeling forecasting floods has long been critical, and recent advances incomputing technologieshavemadethisprocess more acces- sible. According to Ongdas et al. [4], advances, such as cloud computing, the integration of GIS and remote sensing, and tools for emergency management have significantly improved flood hazard mapping and risk mitigation strategies. The foundation of every flood management strategy is the assessment of flood hazards through inundation mapping and the identification of flood risk zones [5]. Floodplain manage- ment is a contemporary and practical approach to river engi- neering that plays a crucial role in assessing flood risks [6]. Because floods can seriously and permanently harm transpor- tation, bridges, agriculture, and other aspects of urban 1. Introduction River floods represent the most frequent and expensive natural disaster affecting most of the countries around the world [1]. They typically occur when intense and prolonged rainfall exceeds the capacityof naturalor engineered drainage systems, leading to the overflow of rivers and subsequent inundation of surrounding areas [2]. Due to the complex nature of flood-generating factors, floods are not entirely preventable. However, their impacts can be significantly reduced if appropriate flood risk manage- ment strategies and accurate information on flood-prone areas are available in advance [3]. Understanding, assessing, and

  2. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2 Advances in Meteorology infrastructure, solutions for flood management andprevention arebadlyneeded. Flood inundation modelingservesan impor- tant role in obtaining spatial distribution information on inun- dation patterns (such as water depth and flow velocity) [7]. This could help determine the danger’s level of severity, any risks to public safety, and possible monetary losses. Addition- ally, they are essential for educating the public and decision- makers as well as for gaining support for the establishment of appropriate governance [3]. Ethiopia, like other sub-Saharan African countries, is highly susceptible to natural disasters, such as droughts and floods. Devastating floods occur insome parts of Ethiopiadur- ingthe months of June, July, August, andSeptember, when the country receives the highest summer rainfall [8]. According to Hagos et al. [8], the Baro–Akobo basin, theAwash River basin, theWabiShebelle,Ribb,andGumarawatersheds,aswellasthe localized flooding dangers of Lake Awassa, Lake Besseka, and DireDawa,areamongEthiopia’smostoftenfloodedareas.The Gumara River basin is one of Ethiopia’s major river basins, located in the Fogera floodplain and prone to flooding [9]. Inundation mapping is primarily accomplished using hydraulic/hydrodynamic models. Their primary applications include planning flood management, estimating susceptible areas, simulating flood occurrences, and identifying geograph- ically distributed variables of relevance [10]. They often solve mathematicalequationsbasedontheconservationofmassand momentum principles to describe the fluid motion and flood wave dynamics [11]. Flood modeling can be performed using different dimen- sional approaches—such as 1D or 2D—depending on the objectives and scale of the study. While 1D models effectively simulate channel flow processes, 2D models are more suitable for analyzingfloodplain dynamics, especiallywhen water over- flowsthechannelandspreadsacrossdownstreamareas[4,12]. To minimize uncertainties in representing terrain and flood behavior, fully 2D models with detailed topographic input arerecommended[13].Thesemodelsarewidelyused for flood extent mapping and risk prediction due to their higher accu- racy in complex flow simulations [14, 15]. Although computa- tionally intensive, 2D models that solve the full shallow water equations offer precise simulation of flood timing and inunda- tion duration. ThisworkmadeuseofHydrlogicEngineeringCenterRiver Analysis System’s (HEC-RAS’s) 2D capability, which may deploy various schematization complexities and has a broad varietyof applications, accordingto the literature review[4, 11, 16]. Sabeti et al. [17] assessed HEC-RAS’s 2D capability using benchmark tests created by the Joint Defra Environment Agency in the United Kingdom. The outcomes showed that HEC-RASoutperformsallotherexaminedmodels(TUFLOW, MIKE FLOOD, SOBEK, etc.). The HEC-RAS offers compre- hensive insights into river flow dynamics, sediment transport, and floodplain flooding. It specializes in hydraulic modeling and river channel studies. Quirogaa et al. [18]found that when compared to the flood extent determined by satellite imagery, the 2D HEC-RAS flood simulation performed effectively. HEC-RASishydraulicmodelingsoftwaredevelopedbythe U.S.ArmyCorpsofEngineers’HydrologicEngineeringCenter River Analysis System [19]. Many academics across the world have used the HEC-RAS model for mapping flood inundation and assessing flood hazards. The most effective computer- programming tool for mapping flood inundation successfully is HEC-RAS. Using HEC-RAS to map flood inundation requires a number of crucial parameters. These include bridge data, physical watershed parameters, topography data, dis- charge data (profiles), Manning’s roughness coefficient, and river geometric cross-section (such as river centerline, flow route lines, river bank lines, and X’S cut line) [20]. The hydrologic modeling system (HMS) model [21], of the HEC was created by the U.S. Army Corps of Engineers’ andcanbeusedinanumberofhydrologicalsimulations,such as stream restoration planning, urban flood analysis, flood frequency assessment, flood warning system planning, and reservoir spillway capacity evaluation. Watershed routing processes and the entire hydrological cycle (rain transforma- tion) can be modeled by the computer program HEC-HMS. We can use the HEC-HMS program to simulate or compute models, such as runoff volume, direct runoff, baseflow, and channel flow. There are many studies conducted with HEC-HMS in dif- ferent parts of the world in urban areas [22], agricultural areas [23], wetlands [24], arid or semiarid regions [25], and tropical regions [26]. In particular, the model has been successfully applied innumerousstudies to forecast hydrographs and flood peak outputs. Several studies have demonstrated the integration of HEC- RAS and HEC-HMS with GIS for effective flood modeling in various regions. Suryawanshi et al. [27] applied this approach in Turkey’s Göksu River basin. Similarly, Ansarifard et al. [14] used a combination of modelsto simulate flood episodes inthe Khoshke Rudan River in Iran. Keraghel and Gaouaou [28] assessed flood vulnerability in the arid region of Ghardaia city, and Mai and De Smedt [12] utilized integrated hydrologi- cal and hydraulic models for flood forecasting in Vietnam. Additionally, Namara et al. [20] developed flood maps for Ethiopia’s Awash Bello floodplain in the upper Awash River basin using the same modeling tools. This paper focuses on the simulation of the occurrence of floods in the Gumara River, which has flooded several times in the past. In this work, the Gumara River basin’s flood hazard mapping and flood modeling were conducted using the HEC-HMS, HEC-RAS, and GIS models. Additionally, hydraulic 2D flow modeling was carried out using HEC- RAS. Based on CORINE data, Manning’s “n” values were calculated, and flood propagation scenarios of flow hydro- graphs acquired from HEC-HMS were mapped. However, despite previous flood hazard assessments in the Gumara River basin, there remains a lack of integration of social and economic vulnerability factors, as well as a paucity of action- able floodplain management recommendations tailored to this region. This study aims to fill these gaps by combining hydraulicmodelingwithanassessmentofstructuralinterven- tions and vulnerability considerations to support more effec- tive flood risk management. Furthermore, this study identified optimal sites for flood management and mitigation measures.

  3. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 3 300,000 900,000 1,500,000 −300,000 240,000 300,000 360,000 420,000 480,000 Ethiopian Basins Tana Basin 1,500,000 Mereb Gash Tekeze 1,380,000 Denakil Tana Basin Aysha Abbay Abbay 1,320,000 Awash Tana Basin Gumara watershed 900,000 Baro Akobo Ogaden Wabi Shebele 1,260,000 Omo Gibe Rif Vally Genale Dawa 1:426,805 1:426,805 N Gumara watershed 1,310,000 1,310,000 W E S 1,300,000 1,300,000 1,290,000 1,290,000 1,280,000 1,280,000 1:426,805 330,000 340,000 350,000 360,000 370,000 380,000 390,000 400,000 410,000 420,000 Outlet River Gumara Subbasin Lake Tana FIGURE 1: Location map of Gumara watershed. 2. Materials and Methods lake, can lead to significant inundation in surrounding areas. The open drainage outlet also creates a hydraulic backwater effectnearthe lakeshore, which can exacerbate floodingduring times of elevated lake levels. These hydrological characteristics make the Gumara watershed a critical zone for flood modeling and management intervention, justifying its selection for this study. The study region was located inside the Farta, Estie, Dera, and Fogera Weredas of the South Gondar Zone and is part of the downstream portion of the catchment, which is a part of the large and flat floodplain (Fogera and Dera flood- plain). The area of interest in this study is shown in Figure 1. 2.1. Study Area. The Gumara River watershed islocated in the Amara National Regional State’s South Gondar zone. Geo- graphically, it is located between latitudes of 11°34’30.971"N and 11°54’4.822"N and longitudes of 37°31’24.944"E and 38°11’0.966"E. Starting at Mount Guna’s highest point (peak elevationof4090m),itdescendstoLakeTana,whichislocated at the mountain’s lowest point (1784m). The Guna mountain- born Gumara River drains this catchment before joining Lake Tana in the area where it produces flooding. The Gumara River’s water level can rise quickly due to several tributary rivers that drain the highlands westward, resulting in flooding in the low-lying alluvial plains that along the river’s path. The lowlandfloodingintheFogeraandDerafloodplainiscausedin part by the Gumara River from the Guna highlands. TheGumaraRiverbasinfeaturesacatchmentwithanopen drainage outlet to Lake Tana, meaning that excess runoff can flow directly into the lake system rather than terminating in a closed basin. This hydrological configuration influences both water availability and flood dynamics, as the river can convey high runoff volumes downstream during intense rainfall events. However, during peak flows—especially in the rainy season—the limited storage and conveyance capacity of the floodplain, combined with topographical constraints near the 2.2. Climate Conditions. Ethiopia, located in the northeastern part of Africa, experiences the influence of winds from the northeast, southeast, and southwest, which carry moisture from the Indian and Atlantic Oceans. During the summer in thenorthern hemisphere, moist winds gradually moveinto the country as the African sector of the intertropical convergence zone(ITCZ)shiftsnorthward.TheGumaraRiverbasinexperi- ences a tropical highland climate characterized by distinct wet and dry seasons, with marked temporal and spatial variations intemperatureandrainfall.Theseclimaticfactorsplayacritical role in influencing flood behavior and water availability across the region. Based on rainfall, the region’s climate may be split into two primary seasons: the summer or wet season, which

  4. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 Advances in Meteorology 1550 1504.39 Annual rainfall (mm) 1500 1452.94 1450 1400 1329.5 1350 1300 1250 1200 Debre Tabor Mrkane Eyesus Station name Wazaye FIGURE 2: Annual rainfall (in mm) in the study area from 1994 to 2018. 35 30 Annual temperature (°C) 25 20 15 10 5 0 Debre Tabor Mekane Eyesus Meterological stations Wazaye Minimum Maximum Average FIGURE 3: Annual mean temperature in the study area from 1994 to 2018. 2.3. Dataset and Sources. Forthecompilationofthisstudy,the following data were collected: satellite data, topographic and GIS data, hydro-meteorological data, field survey data, and census data. Primary data for river cross-sections were system- atically surveyed at 200–300m intervals, with additional sur- veys near hydraulic structures and observation sites. UTM coordinates and 1:50,000 maps were used to align sections perpendicular to flow, ensuring spatial accuracy. Measure- ments were taken at fine resolutions (1m inchannel, 5–50m overbanks) and extended into floodplains. Total stations were critical in capturing high-resolution elevation data and were supplemented by georeferenced imagery for roughness estima- tion.Thisintegratedapproachensuresbothaccuracyandcom- prehensiveness in the survey design. These methods were chosen to ensure data accuracy and reproducibility for subse- quent hydrological analysis. Meteorological data including dailyrainfall,andtemperaturewereobtainedfromtheNational Meteorological Agency (NMA) at the Bahir Dar branch from 1994 to 2018. The stream flow data collected from Ministry of Water and Energy at the Bahir Dar branch for the calibration and validation of HEC-HMS model. The watershed’s digital elevation model (DEM) was obtained from ALOS-PALSAR. It was downloaded from the Alaska Satellite Facility (ASF) web- site (https://www.asf.alaska.edu) at a spatial resolution of 12.5m. For 2D models, the most crucial input data is the lasts from June to September, and the winter or dry season, whichlastsfromOctobertoMay.Thewatershedreceivesabout 80% of the annual rainfall in the summer (rainy) season (June, July, August, and September) and the remaining 20% in the winter (dry) season, from October to May. The average annual rainfall varies, with ~1504.39mm in the highlands of Debre Tabor and 1329.5mm in the southeastern part of the area (Mekane Eyesus). The lowest rainfall period spans from December to February, while the rainy season occurs from June to September. Spatially, rainfall varies significantly within the basin, with higher annual rainfall observed in the upper catchment and escarpment zones, and lower rainfall recorded in the lowland areas near the Gumara floodplain. The spatial distribution of average annual rainfall is shown in Figure 2. The catchment area experiences significant temperature variationsdue todifferencesinaltitude. Temporally,the region experiences moderate temperatures year-round, with mean monthlytemperaturesrangingfrom12°Cinthecoolermonths (June–August) to 27°C in the warmer months (March–May). The mean annual maximum temperature in the study area ranges from 22.42 to 29.88°C, while the mean annual mini- mum temperature ranges from 8.5 to 10.92°C. December, Jan- uary, February, and March are the hottest months. The temperature in August and July is relatively low. Figure 3 dis- plays the mean annual temperature’s spatial dispersion.

  5. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 5 TABLE 1: Data type and their sources. Data name Primary data River cross-section after dyke level Secondary data Stream flow data Meteorological data DEM LULC Study area floodplain surveying data River cross-section before dyke level data Source of data Scale/period Insitu Collected in 2018 1994–2018 Abay Basin Authority 1994–2018 12.5m by 12.5m 2018 Collected in 2018 Collected in 2018 National Metrological Agency of Ethiopia Alaska Satellite Facility (ASF) Landsat 8 (www.earthexplorer.com) Design and Supervision Corporation Design and Supervision Corporation HEC-HMS is provided by HEC-GeoHMS. Coordinate trans- formation,dataformatting,andprocessingareallpossiblewith GIS. Currently, HEC-GeoHMS uses DEM to prepare several hydrologic units and determine subbasin delineation. These hydrologicinputsaresupported byHEC-HMSasafoundation for hydrologic modelling. Arc-Hydro (tools version that works with ArcGIS 10.4) was used to process a DEM to delineate watershed, subwatersheds, stream network, and some other watershed characteristics that collectivelydescribe the drainage patterns of a basin. Using HEC-Geo HMS, the terrain proces- sing findings can be utilized to generate input files for numer- oushydrologicmodels.HEC-RASsoftwareintegrateswithGIS data and provides tools for creating detailed hydraulic models and analyzing the potential impacts of various hydraulic sce- narios. A popular spreadsheet program for computations, data analysis, and visualization is Microsoft Excel. Itallowsuserstoorganizeandmanipulatelargeamountsof data, create charts and graphs, and perform complex mathe- matical calculations. Total stations often have built-in software for data collection and processing, making those essential tools for surveyors. Data continuity can be disrupted by missing data, which can occur due to variousfactors, such as equipment damage or faults during measurement periods at gauging stations. To ensure the reliability of the modeling simulations, it is crucial to assess the consistency, adequacy and completeness of the data, and identify any missing values. Missing data can be estimated using data filling methods. It is important to address missing data because incorrect or incomplete data can intro- duce inconsistencies and yield ambiguous results that may deviate from the actual values being represented. DEM. In general, 2D hydraulic models are just as useful as the DEM,sinceaccuratetopographicaldataaffectstheoutcomesof flood simulations. The LandSat8 satellite images for this study wereacquiredfromtheUSGS(U.S.geologicalsurvey)GLOVIS (global visualization viewer) website (http://glovis.usgs.gov/). Satellite images were used for the land use and land cover (LULC) map. The research area’s soil texture map was obtained as a shape file from the Agriculture Bureau in Bahir Dar. The census data were collected based on a questionnaire designed and distributed among the people living in the floodplain and comments were collected. The NE coordinates and the corresponding flood depths were quarried from the people. These datasets are not publicly available by default but can be accessed upon formal request and approval from the respective agencies. Primary data collected during fieldwork (e.g., river cross-sections) are available upon reasonable request from the corresponding author. This clarification supports transparency and assists readers interested in replicating or building upon the study. To model the Gumara River watershed floods all relevant data type and sources presented in Table 1. 3. Methods To achieve the research objectives, various tools and materials were utilized. These included Arc Map (GIS) for obtaining hydrological and physical parameters, as well as spatial infor- mation of the study area. A DEM with a resolution of 12.5×12.5m was used as input data in Arc Map software to delineate catchments and estimate their characteristics. Fur- thermore, meteorological and hydrological data were gathered and included in the analysis. For simulating the rainfall-runoff process within a watershed, the HEC-HMS software was employed. The simulation of the conversion of rainfall into runoff is made possible by HEC-HMS. Furthermore, for hydraulic analysis of stream channel geometry and water flow, the HEC-river analysis system (HEC-RAS) software was utilized. In summary, the research relied on Arc Map (GIS) for obtaining spatial information and delineating catchments, a 12.5m DEM for catchment estima- tion, and hydrological and meteorological data. A group of ArcGIS tools called HEC-GeoHMS were created especially for analyzing geospatial data and produce input for the HEC- HMS. The link to convert GIS spatial data into model files for 3.1. Data Preparation and Analysis. Before using the data for further analysis, it is important to make sure that the data are homogenous, correct, sufficient, and fill in the missing values. The first step in the data screening process was to visually inspect the data for outliers and missing values. Missed meteo- rological and hydrological data values, as well as any outstand- ing outliers, were disregarded and replaced using the basic regression procedure. Therefore, the meteorological data qual- itytestisveryimportantforthereliablepredictionofthemodel output. 3.2. Estimation of Missed Data. Missing datafrom incomplete data records for gauges that need to be observed on a regular

  6. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 Advances in Meteorology 40,000 Cumulative annual RF of individual 35,000 R2 = 0.9999 30,000 R2 = 0.9999 25,000 R2 = 0.9999 station 20,000 15,000 10,000 5000 0 0 5000 10,000 15,000 20,000 25,000 30,000 35,000 Annual average cummulative rainfall of all stations Debre Tabor Mekane Eyesus Wazaye FIGURE 4: Consistency of rainfall data. basis can arise from either the observer’s failure to visit the gauge or from the destruction of recording gauges and instru- ment failure due to mechanical or electrical malfunctions. Any such cases of instrumental failure reduce the length and infor- mationcontentoftheprecipitationrecord.Thereareanumber of methods for estimating missing rainfall data. Among them are the station average method, the normal ratio method [29], theinverse-distanceweighting(IDW)method[30],andregres- sionmethods[31].Inthisresearch,themeanvalueoftheentire period (the arithmetic mean) was used to fill in the missed records for the stations with less than 10% of missed records, while for the stations with greater than 10% of missed records; the IDW method was used. Because the climate station in the studyareaisspatiallyvaryingwithdistance,theformulaforthe IDW method is given by [32]: have all contributed to inconsistence. To overcome the prob- lem of inconsistence, a technique most widely applied called the double–mass curve (DMC) is used. DMC analysis is a graphical method for identifying or adjusting inconsistencies in a station record by comparing its time trend with those of other stations nearby [33]. The equation is given by: Mc MA× Px ¼ Pcx; ð4Þ where Mc is corrected slope of the double mass curve, MA is original slope of the double mass curve, Px is original recorded precipitation at time period T1 at station x, and Pcx is corrected precipitation at any time period T1 at station x. The data reliability of the maximum daily flow data record is checked by a variability test. This can be determined by computing the relative standard, which is the ratio of the stan- dard error of themean to the mean of the data series. To check theconsistencyofastationbyusingdoublemasscurvemethod a graph is plotted with cumulative of the station that has to be checked in the vertical axis and cumulative of other nearby stationsinthehorizontalaxis.AsshowninFigure4,adeviation fromthenormaltrendhasnotobservedanditispossibletosay that the recorded rainfall data for both stations are consistent. Relative standard error of mean is given as follows: i¼1wi× pi ∑n Px¼∑n ; ð1Þ i¼1wi wi¼1 D2; ð2Þ D2¼ ΔX2þ ΔY2 Þ; ð3Þ ð where Px=missed precipitation, D2is the distance of the station in x and y coordinates, Pi=the rainfall at gauge i. sx p 3.3. Data Quality Test. A data quality test is a preliminary analysis before proceeding to further hydrological analysis. In general, hydrological data records may exhibit gaps, which make them less reliable. The causes can be instrument failure, personal miss reading,and other environmentalfactors. Inthis study, the data quality test is conducted by checking for data variabilityandoutliertestsforeachstation.Changesintheland use that make it impractical to keep the gauge at the previous location, where damage commonly occurs, changes in expo- sure to the gauge, and changes in observation producers could × 100<100% p − value Þ; ð5Þ δn ¼ ffiffiffin ð xm where sx—standard deviation, xm—mean, and n—number of data point. The computed value of relative standard error values are presented in Table 2. The computed value of relativestandard error is4.38% less than 10% of relative standard error. This indicates that these

  7. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 7 TABLE 2: Summary of data adequacy test. expressed in units of the sample standard deviation, is the Grubbs test statistic. One outlier is found at a time using this outlier test procedure. The test is repeated until no further outliers are found, after which the identified outlier is removed from the dataset. Test for homogeneity: data must be indepen- dent and homogeneous in order to be analyzed using fre- quency. The observations are guaranteed to come from the same population, thanks to the constraint of homogeneity. RAINBOW software is utilized to perform the homogeneity test on the stream flow data set. The cumulative deviations of the test are often rescaled when the homogeneity of the data is tested. Parameter description Data size, N Mean, Xm (m3/s) or m Standard deviation, Sx (m3/s) or m Relative standard error (%) Sleekness coefficient, Cs Watershed Gumara 25 87.79 28 4.38 1.48 data series can be regarded as reliable and adequate for further design flood frequency analysis. 3.4. Determination of Areal Rainfall. The rainfall over an area is necessary for hydrological analysis in practice, but rain gauges only provide point sampling of a storm’s areal distribu- tion. The Thiessen polygon approach isone themost common method used for determining average precipitation over an area when there is more than one measurement. The basic idea is to create multiple polygons around a measurement point in the watershed, then take a weighted average of the measurements according to the size of each polygon; that is, measurements inside large polygons are given more weight than measurements inside small polygons. The weighted aver- age is calculated by: k Þ; Sk¼ ∑ Xi − ẋ ð7Þ ð i¼1 where Xi are the records from the series X1, X2…Xn andẋ the mean. The initial value of Sk=0 and last value Sk=n are equal to zero. When the deviation crosses one of the hori- zontal lines the homogeneity of the data set is rejected with, respectively 90%, 95%, and 99% probability. The range of cumulative deviation and maximum cumulative deviation could not be rejected on 90%, 95%, and 99% probability levels, which show the homogeneity of the annual data series, assures that the observations are almost reclassified on the same population. Ṗ ¼∑n i¼1PiAi ∑n i¼1Ai; ð6Þ 3.6. Hydrologic Model HEC-HMS. Wecreated hydrographsof the studied watersheds using the HEC-HMS software. When calculating runoff, the software also takes routing, loss, and flowtransformationintoaccount.TheHEC-HMSmodelsetup consists of the basin model, meteorological model, control requirements, and input data (time series data) [34].Following data verification, the basin and meteorological model files, along with certain HEC-HMS parameters, were used to gener- ate the HEC-HMS model. The SCS unit runoff hydrograph approach was used to calculate theamount of water and the input rate,using a hypo- thetical storm for meteorological data. The SCS-hydrograph is an effective approach for transforming estimated direct runoff into hydrographs that model the temporal distribution of a runoff event. This approach is in line with the goals of this study, since it uses discharge volume, peak flow, flood depth, and velocity estimates to evaluate the risks of flash floods. Area and slope, concentration and lag time, curve number (CN) values, and rainfall volumes are the most crucial inputs [35]. Spatial analysis and other ArcGIS tools were incorporated intoHEC-GeoHMS,which wasusedtogeneratetheinputdata forHEC-HMS.Drainagepathwaysandsubbasinswerecreated using digital elevation data in HEC-GeoHMS, coupled with otherparameters includingthe areaand slope ofthesubbasins, the drainage path slopes, and the longest flow paths of the subbasins. The land use and soil type data were used to create the CN for each subbasin. CN values were utilized to estimate the hydrological parameters used in the model and to ascertain the features of the stream or subbasin [34]. Using where Ṗ is the weighted average, P’s are measurements, and A’s are areas of each polygon. The rainfall is never uniform over the entire area of the basin or catchment, but varies in intensity and duration from place to place. Thus, the rainfall recorded by each rain gauge station should be weighted according to the area, it represents. Figure 5 shows the spatial distribution of selected rainfall sta- tions in the study area using Thiessen polygon. 3.5. Stream Flow Availability and Homogeneity Test. The daily discharge of the study area is collected from the Ministry of Water and Energy. The daily discharge, as opposed to the daily precipitation, has complete data composition for the sta- tions taken into consideration to represent the study area. The discharge gauge is located at outlet of Gumara River down side of the main road from Bahir Dar to Gonder where the down- stream end is considered flood prone area. Observed stream- flowdatafortheGumaraRiverwerealsoemployedtocompare with simulated flows during model calibration and validation, focusing on selected flood events. Some data sample may have high or low outliers, or both, in a sample, and these can have different impacts in data analysis and modeling. The sample meanandstandarddeviation,forexample,maybesignificantly higher than the sample values due to an extremely high value. Therefore, an outlier is done for observation that significantly deviates from the bulk of data due to errors in data collection, recording, and natural causes. The Grubbs T test was used for this particular study to identify outlying flow observation through XLSTAT 2019. The greatest absolute difference from the sample mean,

  8. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 Advances in Meteorology 37°40´0˝E 37°45´0˝E 37°50´0˝E 37°55´0˝E 38°0´0˝E 38°5´0˝E 38°10´0˝E 11°56´0˝N N W E S 11°50´30˝N 11°45´0˝N 11°39´0˝N Station name Mekane Eyesus Wanzaye Debre Tabor 11°34´0˝N 0 4.75 9.5 19 km FIGURE 5: Spatial distribution of gauge stations. meteorological data, the HEC-HMS model for the Gumara River is developed. function is small, the model is validated. During this process, calibrated model parameter values are kept constant. In this study, the Nash–Sutcliffe (NSE) and root mean squared error (RMSE) is applied for the verification of both calibration and validation model. The averageof the squares of mistakes or deviations—that is, the differences between the expected and actual values—is computed using the RMSE. 3.7. HEC-HMS Model Evaluation. Calibration,validation,and sensitivity analysis were all part of the model evaluation pro- cess. To identify the crucial factors that required to be properly calculatedinordertomakeanaccuratebasinyieldprojection,a sensitivity analysis of the model was conducted. Finding the model parameters that have the biggest impact on the model’s output is possible through the use of sensitivityanalysis. Model parameters are ranked according to how much they contribute to the overall prediction error. Model calibration is a systematic method of modifying model parameter values until the model’s outputs reasonably correspond to the data that was observed [36]. The objective function explainsthe quantitativemeasure of thematch. In the precipitation-runoff models, this function measures the degree ofvariationbetweencomputedandobservedhydrographs.The idealparameter values thatminimize the objective function are determined during the calibration process. Modelvalidationistheprocessoftestingthemodel’sability to simulate observed data, other than those used for the cali- bration,withinacceptableaccuracy[34].Eachcalibratedmodel should be validated before it is proposed for use. The model parameter obtained will be used to validate by using different sets of events. The observed hydrograph and the simulated hydrographarecontrasted.Thevalidationofamodelisdepen- dent on the results of the error function, where if the error ENS ¼ 1 −∑n Þ2 Þ2; i¼1Qobs− Qsim ∑n ð ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n ð ð8Þ i¼1Qobs− Qoave v u n u Þ2 ∑ i¼1 Qobs − Qsim ð t ð9Þ ; RMSE ¼ where Qobs is observed value (m3/s), Qoave is average observed value (m3/s), Qsimis simulated value (m3/s), Qsaveis average simulated value (m3/s), and n is the total number of input data points. 3.8. HEC-RAS Model. For the computation and analysisof the floodplain, the HEC-RAS software was utilized. Three primary types of data are needed for HEC-RAS implementation: (i) geometric data, (ii) basin characteristics, and (iii) flow data. The HEC-GeoRAS user interface is used to prepare the necessarygeometricdata,whichincludescross-sectioncutlines and stream centerlines. It is a set of procedures, materials, and

  9. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 9 TABLE 3: Maximum design rainfall depth by Gumbel’s EVI distribution. Gumbel Number of data, N Return period, T (years) Mean, Xm Standard deviation, Sx; (m3/s), m Reduced varate, Yt ¼ −lnðlnðT=ðT − 1ÞÞ:, Frequency factor, KT=0.7801×(0.5772-Yt) Peak value, XT=Xm+KT×Sx Debre Tabor 25 100 63.63 13.5 4.6 3.14 105.99 Mekane Eyesus 25 100 57.19 12.18 4.6 3.14 95.39 Wanzaye 25 100 62.02 12.44 4.6 3.14 101.03 TABLE 4: Maximum design rainfall depth by normal distribution equation. Normal distribution Number of data, N Return period, T (years) Mean of data Xm; (m3/s), m Standard deviation, Sx; (m3/s), m Skewness, Cs Frequency factor, KT=f (T, Cs, N) Peak value, XT=Xm+KT×Sx Debre Tabor 25 100 63.63 13.5 0.17 2.33 95.05 Mekane Eyesus 25 100 57.19 12.18 0.57 2.33 85.53 Wanzaye 25 100 62.02 12.44 0.22 2.33 90.96 instruments for processing geographic data in ArcGIS. It also makesitpossibletoinputtheprepareddataintotheHEC-RAS model. A digital terrain model (DTM) of the river system is needed to create the import file. The cross-section data were modified to fit the available bathymetric profiles after being uploaded into the HEC-RAS system. Manning’s friction coefficient “n” falls under this category. Many variables affect the Manning’s “n” value, such as vegeta- tion, surface roughness, channel irregularities, channel align- ment, scour and deposition, obstructions, channel size and shape, stage and discharge, seasonal variations, suspended material, and bed-load. The Manning’s n value was extracted fromaland-usemapforthisinvestigation.Afterthat,theHEC- RAS model was run, the output hydrographs from HEC-HMS were imported, and the roughness, channel convergence, and divergencecoefficientswereintroduced.Thehydraulicanalysis results were then extracted, the flood zones and flood depth were extracted, and the floodplains were identified for each return period of years using ArcGIS software. section of data preparation and it will be continuously referred in doing the design flow frequency analysis [37]. 3.10. Gumbel (EV-I) Extreme Value Type I Distribution. Koutsoyiannis [38] introduce the concept of Gumbel extreme valuedistributionanddevelopedamodelforpredictinghydro- logical extreme events like flood peaks and maximum rainfall. Using data provided in statistical data summary table of three stations, the maximum design rainfall depth was calculated based on the Gumbel’s EVI distribution, as shown in Table 3. The Gumbel’s EVI distribution method of design rainfall depth of Debre Tabor, Mekane Eyesus, and Wanzaye station frequency analysis over 100-year return period gives us 105.99, 95.39, and 101.03mm, respectively. 3.11. Normal Distribution. Golian et al. [39] introduce the normal probability distribution for the frequency analysis of extreme values. This distribution is more preferred for sym- metrical or nonskewed data sets and it can be usually used for frequency analysis of hydrological extreme events as flood peaks andmaximum rainfalldataseries. The maximum design Rainfall depth was calculated based on the normal probability distribution, as shown in Table 4. The normal distribution method of designrainfall depth of Debre Tabor, Mekane Eyesus, and Wanzaye station frequency analysis over 100-year return period gives us 95.05mm, 85.53 mm, and 90.96mm, respectively. 3.9. Analysis of River Peak Discharge. In hydrological fre- quency analysis, a certain parent probability distributions model can fit the annual series of extreme values. Among a number of parent probability distributions mod- els Gumbel (EV-I), normal, lognormal, Pearson type-III, and log-Pearson type-III are widely applicable in hydrological fre- quency analysis [37]. The design flood computed from flood frequency analysis distribution models including Gumbel(EV-I), normal, lognor- mal,PearsontypeIII,andlog-Pearsontype-IIIdistributionand finally the best fit probability distribution for each of indepen- dent station will be selected. The statistical data summary table of maximum daily flow data records of the three station is provided in the table under 3.12. Lognormal Distribution. This distribution can be done afterthenormalseriesofdataaretakenoutoflogarithmicvalue and then it has the same procedure as that of any theoretical frequency analysis. The frequency factor of the lognormal dis- tribution depends on the design return period at zero coeffi- cientofskewness[40].Themaximumdesignrainfalldepthwas

  10. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 10 Advances in Meteorology TABLE 5: Maximum design rainfall depth by lognormal distribution. Lognormal distribution Number of data, N Return period, T (years) Mean of data Xm; (m3/s), m Standard deviation, Sx; (m3/s), m Skewness, Cs Frequency factor, KT=f (T, Cs, N) Peak value, XT=Xm+KT×Sx Debre Tabor 25 100 1.79 0.09 −0.18 2.33 103.11 Mekane Eyesus 25 100 1.75 0.09 0.45 2.33 90.76 Wanzaye 25 100 1.78 0.09 −0.28 2.33 98.13 TABLE 6: Maximum design rainfall depth rainfall by Pearson-III distribution. Pearson type-III Number of data, N Return period, T (years) Mean of data Xm; (m3/s), m Standard deviation, Sx; (m3/s), m Skewness, Cs Frequency factor, KT=f (T, Cs, N) Peak value, XT=Xm+KT×Sx Peak value, XT=Xm+KT×Sx Debre Tabor 25 100 57.19 12.18 0.57 2.74 90.55 100.23 Mekane Eyesus 25 100 62.02 12.44 0.22 2.49 92.93 97.15 Wanzaye 25 100 1.78 0.09 −0.28 2.12 1.97 94.06 TABLE 7: Maximum design rainfall depth by Log-Pearson-III distribution. Log-Pearson type -III Number of data, N Return period, T (years) Mean of data, Ym Standard deviation, Sy Coefficient of skewness, Cs Frequency factor, KT=f (T, Cs) Peak magnitude, YT=Ym+KT×Sy Peak value, XT=Xm+KT×Sx Debre Tabor 25 100 1.79 0.09 −0.18 2.2 2 100.23 Mekane Eyesus 25 100 1.75 0.09 0.45 2.65 1.99 97.15 Wanzaye 25 100 1.78 0.09 −0.28 2.12 1.97 94.06 calculated based on the lognormal distribution, as shown in Table 5. Thelognormaldistributionmethodofdesignrainfalldepth of Debre Tabor, Mekane Eyesus, and Wanzaye station fre- quency analysis over 100 year return period gives us 103.11, 90.76, and 98.13mm, respectively. frequency analysis over 100 year return period gives us 96.77, 90.55, and 92.93mm, respectively. 3.14. Log Pearson Type-III Distribution. The log-Pearson type IIIdistribution isalsoknown as the gammadistribution. Inthe United States, this method is mandatory for projects that involve precipitation frequency analysis. The U.S. weather resources amended andimprovised the Pearson type-IIIdistri- bution by log transforming volume of water into log-Pearson type-III distribution [42]. Thisdistribution can be applied afterthedataseries, initially in logarithmic form, is transformed back to its normal values. The procedure then follows the same steps as any standard theoretical frequency analysis. The Pearson type-III distribution frequency factor depends on the coefficient of skewness derived from the data series and the selected design return period. The maximumdesignrainfalldepth wascalculated based onthelog- Pearson-III distribution, as shown in Table 7. The log-Pearson-III distribution method of design rainfall depth of Debre Tabor, Mekane Eyesus, and Wanzaye station 3.13. Pearson Type-III Distribution. The Pearson III (PE3) distribution is one of the distributions that is most frequently employed in the statistical analysis of extreme data, along with the lognormal, GEV, and log-Pearson distributions [41]. Pearson distribution is a family of gamma distribution, which is suitable for right skewed data distributions. This dis- tribution can be done by using the normal series of data. ThePersontype-IIIdistributionfrequencyfactordependson thedesignreturnperiodand thecoefficientofskewnessthatdata series gives [41]. The maximum design rainfall depth was calcu- lated based on the Pearson-III distribution, as shown in Table 6. The Pearson-III distribution method of design rainfall depth of Debire Tabor, Mekane Eyesus, and Wanzaye station

  11. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 11 Probability density function 0.32 0.28 0.24 0.2 0.16 f (x) 0.12 0.08 0.04 0 50 60 70 80 90 x Histogram Gumbel Max Log-Pearson 3 Lognormal Normal FIGURE 6: Debre Tabor rainfall station probability distributions. Probability density function 0.52 0.48 0.44 0.4 0.36 0.32 f (x)0.28 0.24 0.2 0.16 0.12 0.08 0.04 0 44 48 52 56 60 64 68 72 76 x Histogram Normal Lognormal Log-Pearson 3 Gumbel Max FIGURE 7: Mekane Eyesus rainfall station probability distributions. Probability charts can be used to assess data fit measures, such as the normal, lognormal, or Pearson distributions. Hydrologists frequently employ the technique of comparing the best-suited probability distribution to the given extreme data series. By comparing the quantiles of the sample data with the quantiles of the standardized theoretical distribution on the probability paper, one may generate probability charts. The probability paper merely transforms the linear scale for quantiles in the standard distribution back into a nonlinear scale for position visualization [37, 43]. The probability distri- butions of Debre Tabor, Mekane Eyesus, and Wanzaye are presented in Figures 6– 8), respectively, and the probability distributions goodness-fit test values are presented in Table 8. The best-fitting parent probability distribution among the common probability distributions normal for Debere -Tabore, frequency analysis over 100 year return period gives us 100.23, 97.15, and 94.06mm, respectively. Finally,amongthefourprobabilitydistribution,thebest-fit distribution for each of independent station will be selected based on goodness of fit test. 3.15. Goodness of Fit Test. Selecting the parent distribution that best fits the observed maximum rainfall depth data series is necessary after doing frequency analysis of maximum flood discharge using various distribution techniques. The best- fitting parent probability distribution among the common probability distributions Gumbel EV-I, Normal, lognormal, Pearson type III, and log-Pearson type III are found by using probabilityplotsandtheKolmogorovSmirnov,AndersonDar- ling, and Chi-squared tests.

  12. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 12 Advances in Meteorology Probability density function 0.36 0.32 0.28 0.24 0.2 f (x) 0.16 0.12 0.08 0.04 0 40 44 48 52 56 60 64 x 68 72 76 80 84 Histogram Gumbel Max Log-Pearson 3 Lognormal Normal FIGURE 8: Wanzaye rainfall station probability distributions. TABLE 8: Summary of probability distributions goodness fit test. Kolmogorov Smirnov Statistic Anderson Darling Statistic Chi-squared Statistic Distribution Rank Rank Rank Debre Tabor station Gumbel Max Log-Pearson 3 Lognormal Normal Wanzaye station Lognormal Gumbel Max Log-Pearson 3 Normal Mekane Eyesus station Gumbel Max Log-Pearson 3 Lognormal Normal 0.1292 0.0947 0.1022 0.0875 4 2 3 1 0.7241 0.3016 0.3642 0.2873 4 2 3 1 0.0319 0.2048 0.1317 2.1599 4 3 2 1 0.1419 0.1442 0.1496 0.1734 1 2 3 4 0.4545 0.6121 0.4598 0.5431 1 4 2 3 0.2693 1.2273 0.6728 0.5365 1 4 3 2 0.1858 0.178 0.1898 0.2128 2 1 3 4 1.3232 1.239 1.4723 1.5424 2 1 3 4 2.8546 2.6671 7.0744 6.6627 2 1 4 3 the HEC-HMS modeling approach along with the generation oftheinputfileofHEC-HMSwithHEC-GeoHMSisdiscussed, followed by the modeling approach in HEC-RAS, and gener- atingHEC-RAS inputs with HEC-GeoRAS is brieflydescribed. Last, the section also elucidates the mapping of the floodplain with HECRAS results in HEC-GeoRAS. Lognormal for Wanzaye station, and log-Pearson type III for Mekane Eyesus station were found by using probability plots and the Kolmogorov Smirnov, Anderson Darling, and Chi- squared tests. The best-fitting probability distribution of Gumara stream flow is shown in Figure 9. The best-fitting parent probability distribution among the common probability distributions log-Pearson type III for Gumara stream flow station is found by using probability plots and the Kolmogorov Smirnov and Anderson Darling tests. The model structure is shown in Figure 10, showing the coupling of the model components used in the study. First, the runoff is obtained from the precipitation data with the HEC- HMS model. The obtained runoff is then simulated in HEC-RAS. The output of HEC-RAS is then exported to HEC-GeoRAS for floodplain mapping. In this section, first 4. Results 4.1. Sensitivity Analysis of Model Parameter. The parameter sensitivity used for theflow was selected based on theliterature and HEC-HMS documentation. Using default parameter values, the first simulation was run to see how sensitive the model was to various factors. The values were then adjusted using three different techniques, all falling between higher and

  13. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 13 Probability density function 0.44 0.4 0.36 0.32 0.28 0.24 f (x) 0.2 0.16 0.12 0.08 0.04 0 220 240 260 280 300 320 340 x 360 380 400 420 440 460 Histogram Gumbel Max Normal Lognormal Log-Pearson 3 FIGURE 9: Gumara stream flow station probability distributions. Meteorological data rainfall LULC and soil data DEM Streamfow data HEC-GeoRAS Curve number Areal rainfall River center line River bank Flow path River cross-section GIS preprocessed spatial hydrology database HEC-HMS Calibration and validation HEC-RAS HEC-GeoHMS Discharge hydrograph Flood inundation and mapping FIGURE 10: HEC-HMS and HEC-RAS work flow chart. lower bounds that were determined based on the attributes of eachparameter.Inthefirstoperation,anincrementisaddedto the parameter’s original value. Multiplying the starting value by a predetermined amount is thesecond technique.Adifferentvalue isused in placeofthe starting value in the third technique [37, 43]. As shown in Figure 11, the CN was identified as the most sensitive parameter through iterative simulations, as it directly influ- ences runoff generation in response to precipitation. This find- ing is consistent with hydrological theory, especially in catchments where land use and soil types dominate runoff behavior [34, 36, 44]. The calibration process used well- establishedstatisticalperformancemetrics(e.g.,NSEefficiency, RMSE),ensuringthatthemodeloutputswerebothreliableand

  14. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 14 Advances in Meteorology 2 1 1 0 0 0 −30% −20% −20% −10% −10% 0% % 0 10% 10% 20% 20% 30% −1 −1 −2 −2 −3 −3 −4 −4 −5 CN Peak to ratio Lag time Initial discharge Maskinghumk Initial abstraction MaskinghumX FIGURE 11: Parameter sensitivity. In this investigation, the calibration and validation times for the model yielded NSE values more than 0.75, while the correspondingRMSEvalueswere0.2and0.4.Therewasahigh correlation between the simulated and actual stream flow data, according to the calibration and validation results. Conse- quently, the HEC-HMS model performance grade is catego- rized as a very excellent model based on these statistical error test criteria. The HEC-HMS model can effectively simulate hourly stream flow using rainfall data for this research region, as the model’s predictive power was shown to be quite well during the calibration and validation phase. As a result, the model’s performance was approved and it may be applied to forecast peak floods in the future under various management scenarios. Furthermore, the simulated stream flow data of the research region may accurately show the real stream flow data. justified. The parameter sensitivity value of the model pre- sented in Table 9. 4.2. HEC-HMS Calibration. Calibration was performed for a periodof8years(1994–1911)fortheGumarawatershedofthe area. By using calibrated parameters, flood estimation can be done. The first step is to calculate the rainfall with return per- iods of 2, 5, 10, 20, 25, 50, and 100 years at each available post with various probability distributions. The model results as obtained from the final automatic calibration using the peak weighted RMSE objective function showed that the simulated and observed Gumara watershed. This was demonstrated by NSE, RMSE, and percentage of biased efficiency values for catchments. Model performance evaluation during calibration is presented in Figure 12. 4.3. HEC-HMS Validation. The estimated calibration result was confirmed when all applicable statistical error tests were discovered to be within an acceptable range throughout the validationandcalibrationperiod.Thisdemonstrateshoweffec- tively the HEC-HMS model can handle data from simulated stream flows intheresearch region. Model performance evalu- ation during validation presented in Figure 13. RMSEandNSEareusedtoassessthemodel’sperformance. The value of RMSE should be 0–0.5, and the values of NSE should be between 0.75 and 1.0 for a highly good model, according to research on model assessment recommendations for systematic quantification of accuracy in watershed simula- tions [45]. When NSE value during calibration and validation fall between 0.75 and 1, themodel is categorized as having very excellent performance [46]. 4.4. HEC-HMS Model Result 4.4.1. Back Ground Map File. Thebackgroundmapfilerepre- sents the physical watershed under consideration. For this study a background map file with 25 subbasins and 13 reaches were generated using HEC-GeoHMS as Figures 14 and 15 shows. The background map file with its elements like reaches and junctions. It encompasses basin model file, meteorological modelfile,andgagemodelfilelaterusedasinputinHEC-HMS during rainfall runoff simulation. 4.5. HEC-RAS Calibration. By altering the Manning’s coeffi- cient, the flood extent was calibrated.After testing, the channel and flood-plain Manning coefficients were found to be the most suitable at 0.06 and 0.035, respectively.

  15. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 15 TABLE 9: Parameter sensitivity. Initial discharge 0.798 0.798 0.798 0.798 0.789 0.744 0.798 0.799 0.799 0.799 0.799 Initial abstraction 0.882 0.898 0.898 0.882 0.891 0.744 0.822 0.821 0.86 0.86 0.859 Thresholds (%) CN Lag time MaskinghumK MaskinghumX Peak to ratio −4.127 −1.94 −0.66 0.068 0.484 0.744 0.755 0.842 0.799 0.837 0.815 25 20 15 10 5 0 −5 −10 −15 −20 −25 0.807 0.835 0.819 0.788 0.768 0.744 0.794 0.79 0.728 0.77 0.728 0.81 0.781 0.757 0.765 0.772 0.744 0.796 0.788 0.791 0.801 0.791 0.771 0.773 0.774 0.776 0.771 0.744 0.806 0.806 0.809 0.81 0.811 0.767 0.774 0.763 0.793 0.772 0.744 0.801 0.806 0.844 0.804 0.808 FIGURE 12: Model performance evaluation during calibration.

  16. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 16 Advances in Meteorology FIGURE 13: Model performance evaluation validation. Ground truth data collection: collecting ground truth data involves obtaining accurate and reliable information about the Earth’s surface for comparison with the satellite imagery. The relationship between satellite imagery and HEC-RAS outcomes lies in their complementary roles in flood mapping. Satellite imagery can provide initial information about the flood extent, while HEC-RAS models can further refine and validate this information by simulating the hydraulic behavior of the flood. performanceoftheHEC-RASmodelinpredictingfloodevents displayed in Figure 16 below. 4.7. HEC-RAS Model Result 4.7.1. Flood Depth Map Before Dyke Built. In the study area, thefloodimpactforeachreturnperiod(2,5,10,25,50,and100 years) is assessed based on the extent of the flooded areas and thecorrespondingflood depths and velocity.The flooded areas are classified into different categories based on the water depth associated with each flood event. The findings revealed that for the 2-year return period, the maximum flood depth exceeded 1.3m, with a velocity of 1.75m/s and an area coverage of 62.33km2. Similarly, for the 100-year return period, the maximum flood depth measured above1.3m,withavelocityof1.75m/sand anareacoverageof 68.04km2. The flood inundation modeling, utilizing HEC-RAS, was conducted for the downstream Gumara River in the lake Tana 4.6. HEC RAS Validation. The NDWI indices that best matched the simulated flood extent with the flood extent after severalindiceswereappliedwereselected.Tovalidatetheaccu- racy of the simulated flood magnitudes, satellite images recorded in 2011 were utilized. These images captured a flood event that was deemed similar in magnitude to a 100-year simulatedflood.Bycomparingthesimulatedfloodmagnitudes with the observed flood captured in the satellite images, an assessment could be made regarding the reliability and

  17. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 17 FIGURE 14: Back ground map file Gumara River watershed. for each return periods were 5.14%, 4.73%, 5.46%, 5.97%, 7.73%, and 4.71%, respectively. subbasin[47–49].Thestudyfocusedoncalculatingflooddepth andvelocitymagnitudefortwodifferentreturnperiods.Forthe 10-year return period, flood depth ranged from 4.118 to 8.074m, and the corresponding velocities ranged from 9.678 to 18.41m/s. For the 100-year return period, flood depth ran- gedfrom4.591to8.071m,andthevelocitiesrangedfrom9.592 to 18.39m/s. Figures 17–22 show the flood depth map before the dyke was built in 2, 5, 10, 25, 50, and 100 years return periods, respectively. 5. Discussion Frequent flooding in villages located along the Gumara River, particularly near its confluence with the main river channel, prompted this study. These settlements, situated in the lower basin, are especially vulnerable due to their proximity to areas where runoff accumulation is significant. To analyze this risk, we employed the HEC-HMS model to simulate peak flow dis- charges, which represent the maximum rate of runoff during storm events and serve as a critical indicator for flood risk assessment. The hydrographs and peak discharges corresponding to different return periods were derived and examined at each sub-basinoutlet tounderstand the temporal and spatialbehav- ior of floodwaters. The HEC-HMS model was carefully cali- bratedandvalidatedusinghistoricalfloodeventstoensurethat it could replicate observed hydrological extremes in the region. Inparallel,HEC-RASwasutilizedtomodelthespatialextentof inundation resulting from design storms of varying intensities. These hydraulic simulations offered a detailed visualization of flood-prone areas and contributed to evaluating the effective- ness of existing flood protection infrastructure. The accuracy of both models was found to be sensitive to input data quality, particularly the resolution of the DEM. We 4.7.2. Flood Depth Map After Dyke Built. Inthestudyarea,the impact of flooding following the construction of a dyke is eval- uated for different return periods (2, 5, 10, 25, 50, and 100 years). This assessment considers the extent of the flooded areas and the corresponding flood depths. The flooded areas are categorized based on the water depth associated with each flood event. This classification scheme provides a detailed understand- ing of the flood impact in terms of both the area coverage and the depth of the floodwaters. In detail, shown that below from Figures 23– 28. Table 10 below shows impact of flood before construction of dyke for each return periods 2, 5, 10, 25, 50, and 100 years were 6232.64, 6262.51, 6375.04, 6495.62, 6639.55 and 6804.26 ha, respectively, and after dyke construction also 5912.18, 5966.47, 6027.13, 6108.04, and 6483.49ha at the same return periods. The construction of dyke reduced impact of flooding

  18. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 18 Advances in Meteorology FIGURE 15: Subbasin W260 hydrograph. employed a 12.5m resolution DEM, enhanced with building footprints and other surface features, to improve terrain repre- sentation. This refinement, based on remote sensing-derived data, allowed us to better simulate overland flow and flood extents. Manning’s roughness coefficients were assigned using the most current CORINE land cover data and supported by values drawn from published literature [50], ensuring that channel and surface characteristics were accurately represented. The simulated hydrographs produced by the HEC-HMS modelwereconsistentwiththosefound insimilarhydrological studies, e.g., Gebre [51], and Tassew et al. [52], suggesting that ourcalibrationwasappropriateforthestudyarea.Theseresults support the reliability of theHEC-HMS model for flood analy- sisintheEthiopiancontext.Thefrequencyanalysisofhistorical peakflowsidentifiedthelog-PearsontypeIIIdistributionasthe best fit for estimating design discharges across various return periods, aligning with previous research recommenda- tions [52–54]. Field observations and model outputs indicate that flood events in the study area are often the result of undersized river cross-sections, rather than misrepresentations in hydrological parameters. In areas where the river narrows, the incoming flow exceeds channel capacity, leading to lateral overflow, par- ticularly along the left bank. Low-permeability surfaces in urbanized zones exacerbate this overflow, reducing infiltration and increasing runoff. These findings highlight the need for targeted interventions, such as channel widening, regular sedi- mentremoval,andtheconstructionofsupplementarydrainage systems—particularly along the right bank—to enhance con- veyance and reduce flood risk. Adjustingthelongitudinal slope of the channel could also help regulate flow velocities and reduce erosive forces. The combination of hydrologic and hydraulic modeling in this study provides a valuable framework for flood hazard map- ping and management. When integrated with GIS tools, these models can offer real-time flood predictions and support decision-making in other flood-prone regions. Similar modeling strategies have been applied successfully in various international contexts, such as the Göksu River basin in Turkey [54], the Bostanli basin [55], and the Khoshke Rudan River in Iran [56]. Inourcase,thisresearchrepresentsanovelapplicationofcoupled

  19. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 19 340,000 344,000 348,000 352,000 Sentinel-2 image in 2011 E.C fogera and dera foodplain 1,318,000 1,318,000 N W E S 1,315,000 1,315,000 1,312,000 1,312,000 Gumara River Highland Flatland Flooded area Deep food 1,309,000 1,309,000 1:72,503 340,000 344,000 348,000 352,000 FIGURE 16: Satellite flood map for validation simulated flood map. 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 2 years return period food depth map before dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 17: Flood Depth map before dyke Built in 2 years return periods. HEC-HMS and HEC-RAS modeling to the Gumara River, con- tributing new insights to regional flood risk assessment. Lookingahead,themodelingresultscaninformbasin-scale floodmanagementstrategies.Theseincludereinforcingnatural floodplainsthroughdykeconstruction,afforestation,maintain- ing agricultural land use in low-lying areas to reduce exposure, and implementing structural interventions in high-risk zones. Themethodologydemonstrated here can be adapted to similar

  20. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 20 Advances in Meteorology 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 5 years return period food depth map before dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele5 Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 18: Flood depth map before dyke built in 5 years return periods. 338,000 338,000 340,000 340,000 342,000 342,000 344,000 344,000 346,000 346,000 348,000 348,000 350,000 350,000 352,000 352,000 354,000 354,000 10 years return period food depth map before dyke built 1,318,000 1,318,000 1,318,000 1,318,000 N W E 1,316,000 1,316,000 1,316,000 1,316,000 S 1,314,000 1,314,000 1,314,000 1,314,000 1,312,000 1,312,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,310,000 1,310,000 1,308,000 1,308,000 1,308,000 1,308,000 1:70,846 338,000 338,000 340,000 340,000 342,000 342,000 344,000 344,000 346,000 346,000 348,000 348,000 350,000 350,000 352,000 352,000 354,000 354,000 FIGURE 19: Flood depth map before dyke built in 10 years return periods.

  21. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 21 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 25 years return period food depth map before dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele25 Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 20: Flood depth map before dyke built in 25 years return periods. 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 50 years return period food depth map before dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 21: Flood depth map before dyke built in 50 years return periods.

  22. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 22 Advances in Meteorology 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 100 years return period food depth map before dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 22: Flood depth map before dyke built in 100 years return periods. 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 2 years return period food depth map afer dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebeke Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 23: Flood depth map after dyke built in 2 years return periods.

  23. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 23 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 5 years return period food depth map afer dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 24: Flood depth map after dyke built in 5 years return periods. 338,000 338,000 340,000 340,000 342,000 342,000 344,000 344,000 346,000 346,000 348,000 348,000 350,000 350,000 352,000 352,000 354,000 354,000 10 years return period food depth map afer dyke built 1,318,000 1,318,000 1,318,000 1,318,000 N W E 1,316,000 1,316,000 1,316,000 1,316,000 S 1,314,000 1,314,000 1,314,000 1,314,000 1,312,000 1,312,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,310,000 1,310,000 1,308,000 1,308,000 1,308,000 1,308,000 1:70,846 338,000 338,000 340,000 340,000 342,000 342,000 344,000 344,000 346,000 346,000 348,000 348,000 350,000 350,000 352,000 352,000 354,000 354,000 FIGURE 25: Flood depth map after dyke built in 10 years return periods.

  24. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 24 Advances in Meteorology 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 25 years return period food depth map afer dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 26: Flood depth map after dyke built in 25 years return periods. 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 50 years return period food depth map afer dyke built 1,318,000 1,318,000 N W E 1,316,000 1,316,000 S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:70,846 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 27: Flood depth map after dyke built in 50 years return periods.

  25. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Advances in Meteorology 25 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 100 years return period food depth map afer dyke built 1,318,000 1,318,000 N 1,316,000 1,316,000 W E S 1,314,000 1,314,000 1,312,000 1,312,000 Kebele Fd100t Flood depth (m) 0.001–0.25 0.25–0.5 0.5–0.75 0.75–1 1–1.3 >1.3 1,310,000 1,310,000 1,308,000 1,308,000 1:71,005 338,000 340,000 342,000 344,000 346,000 348,000 350,000 352,000 354,000 FIGURE 28: Flood depth map after dyke built in 100 years return periods. TABLE 10: Flood magnitude and corresponding inundated area. Inundated area (ha) Before dyke (A) 6232.64 6262.51 6375.04 6495.62 6639.55 6804.26 Impact of dyke in (%) (C/A)×100 Difference (ha) (A−B)= C 320.455 296.035 347.91 387.575 513.518 320.769 Peak discharge (m3/s) Sr. no Return periods After dyke (B) 5912.18 5966.47 6027.13 6108.04 6126.03 6483.48 1 2 3 4 5 6 2 5 359.7 366.5 382.6 410.8 452.4 527.3 5.14 4.73 5.46 5.97 7.73 4.71 10 25 50 100 watersheds across Ethiopia and beyond, offering a scalable approachtoimprovingfloodresiliencethroughevidence-based planning. However, due to potential changes in climate patterns and landusedynamics,amoreadaptiveapproachthatincorporates long-term variability in rainfall, land cover, and other key fac- torsmaybenecessary.Suchchangescouldintroduceadditional sources of uncertainty that should be considered in future studies. 6. Limitations The findings and mitigation measures presented in this study are specific to the Gumara River in Ethiopia. However, it is important to note the limitations regarding the transferability and generalization of the integrated modeling approach to other regions with differing climatic conditions, topography, and hydrological characteristics. Hydrological models often involveassumptions,suchasthoserelatedtochannelgeometry and the representation of hydraulic structures that must be carefully evaluated for local applicability. This study utilizes historical data to assess flood risk based on return period storms of 2, 5, 10, 25, 50, and 100 years, under the assumption that future land use and climate will follow similar trends. 7. Conclusions The study area experiences frequent flooding primarily caused by the Gumara River. To adequately address and miti- gate flood risks, it is essential to employ nonstructural approaches, such as flood inundation mapping and flood risk analysis. These techniques are vital in ensuring prepared- ness to handle recurring high-level floods. By utilizing hydraulic modeling software like HEC-RAS and GIS tools, precise mapping of areas prone to flooding has been achieved for different return periods.

  26. 1306, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/adme/5092932 by National Institute Of Technology,Arunachal Pradesh, Wiley Online Library on [16/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 26 Advances in Meteorology The combination of intense rainfall and the steep slope contributes to the occurrence of flooding in the Fogera and Dera floodplains. The excess water from the upper catchment flows downstream, causing the floodplain to inundate. The model utilized peak estimated time series discharge values for various return periods, including 2, 5, 10, 25, 50, and 100 years. The specific values for each return period were as follows: 359.5m3/s (2-year), 366.6m3/s (5-year), 382.6m3/s (10-year), 410.8m3/s (25-year), 452.4m3/s (50- year), and 527.3m3/s (100-year). The construction of dyke reduced the impact of flooding foreachreturnperiodwere5.14%,4.73%,5.46%,5.97%,7.73%, and 4.71%, respectively. Then, the analysis shows that dyke built on the Gumara River not fully prevented the impact of flooding. Due to the high flood risk in the study area, particularly duringthesummerseasonfromJulytoAugustindifferentyears, it is recommended to construct a storage dam above Gumara Bridge. The dam will serve various purposes, including: [2] H. Desalegn and A. Mulu, “Mapping Flood Inundation Areas Using GIS and HEC-RAS Model at Fetam River, Upper Abbay Basin, Ethiopia,” Scientific African 12 (2021): e00834. [3] A. Mishra, S. Mukherjee, B. Merz, et al., “An Overview of Flood Concepts, Challenges, and Future Directions,” Journal of Hydrologic Engineering 27, no. 6 (2022): 03122001. [4] N. Ongdas, F. Akiyanova, Y. Karakulov, A. Muratbayeva, and N. Zinabdin,“Application of HEC-RAS(2D) forFlood Hazard Maps Generation for Yesil (Ishim) River in Kazakhstan,” Water 12, no. 10 (2020): 2672. [5] N. N. Kourgialas and G. P. Karatzas, “Flood Management and a GIS Modelling Method to Assess Flood-Hazard Areas—A Case Study,” Hydrological Sciences Journal 56, no. 2 (2011): 212–225. [6] M. O. Jimoh, S. O. Bilewu, A. Saheed, and H. S. Aremu, “Multidimensional Approach to Mitigating Flood Risks and Impact: A Case Study of Rice Farming in Nigeria,” Journal of Advanced Environmental Research and Technology 1 (2023): 27–38. [7] H. Hamidifar, M. Nones, and P. M. Rowinski, “Flood Modeling and Fluvial Dynamics: A Scoping Review on the Role of SedimentTransport,”Earth-ScienceReviews253(2024):104775. [8] Y. G. Hagos, T. G. Andualem, M. Yibeltal, and M. A. Mengie, “Flood Hazard Assessment and Mapping Using GIS Integrated With Multi-Criteria Decision Analysis in Upper Awash River Basin, Ethiopia,” Applied Water Science 12, no. 7 (2022): 148. [9] A. Getachew, Impacts of Upstream Watershed Management Activities on Downstream Water Users and Users Willingness to Pay by Contingent Valuation Method (The Case of Gumera River Watershed Ethiopia) (Addis Ababa University, 2014). [10] A. H. Tanim, Coastal and Inland Flooding-From Advanced Hydrodynamics and Data-Driven Modeling to Multi-Variate CoastalVulnerabilityAssessment(UniversityofSouthCarolina, 2023). [11] A. A. Al-Hussein, S. Khan, K. Ncibi, N. Hamdi, and Y. Hamed, “Flood Analysis Using HEC-RAS and HEC- HMS: A Case Study of Khazir River (Middle East—Northern Iraq),” Water 14, no. 22 (2022): 3779. [12] D. T. Mai and F. De Smedt, “A Combined Hydrological and Hydraulic Model for Flood Prediction in Vietnam Applied to the Huong River Basin as a Test Case Study,” Water 9, no. 11 (2017): 879. [13] F. Peña, F. Nardi, A. Melesse, et al., “Compound Flood Modeling Framework for Surface–Subsurface Water Interac- tions,” Natural Hazards and Earth System Sciences 22, no. 3 (2022): 775–793. 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Kjeldsen, “Reconstructing the 1968RiverChewFlashFlood:MergingaHEC-RAS2DHydraulic Modelling Approach With Historical Evidence,” Geomatics, Natural Hazards and Risk 15, no. 1 (2024): 2377655. • Providing flood routing services to protect downstream communities from flooding. • Implementing a silt excluder and conducting river chan- nelization, which would be more advantageous com- pared to constructing a dyke. • Constructing dykes and other structures along the Gumara River tributaries to further reduce flooding. Data Availability Statement Thedatausedtosupportthefindingsofthisstudyareavailable from the corresponding author upon request. Disclosure All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflicts of interest. Author Contributions Tade Mule Asrade has made considerable contributions in designing the study, data acquisition, data collection, analysis, interpretation, and manuscript writing. 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