Hydrologic Engineering Center Research Articles

Assessment of surface water resource and allocation optimization for diverse demands in Ethiopia's upper Bilate Watershed

Water planning and management is very crucial for all computing sectors for water uses to maximize the scarce and allocated water uses based on their demands sustainably in Ethiopia's upper Bilate watershed. The shortage of surface water, especially during dry months, has become a major point of contention between upstream and downstream water users in the upper Bilate River. Therefore, the key objectives of the study are to evaluate the surface water resources and determine the best distribution for a range of requirements in the watershed. The historical climatic and stream flow daily data for the period of 2010–2019 have been used for the analysis. Hydrologic Engineering Center Hydrologic Modeling System-Geospatial Hydrologic Modeling Extension with the Hydrologic Engineering Center Hydrologic Modeling System was used for rainfall-runoff analysis. The model output further represents that the yearly overall surface water of the watershed is 502 MC M. Estimated annual environmental requirement is 75.32MCM which is 15% of the average annual available flow in watershed. Current annual irrigation, livestock, and domestic water demand were estimated to be 19.34 MC M, 12.39 MC M, and 79.4 MC M, respectively. The net amount of water delivered was 19.25 MC M, 79.28 MC M, and 12.36 MC M for irrigation, domestic, and Livestock demands, respectively, in the reference scenario. In the currently available (reference) scenario, 99.8% of the water supply need had been fulfilled, yet only 0.2% of the requirements for water were unmet. Average annual water demand of 111.13 MC M in the current scenario growth to 176.08 MC M when the future growth scenario. In contrast, for the future irrigation development and population projections scenario, 69.8% of the supply-demand became acquainted, and 30.2% of the demand for water remained unfulfilled in 2035. Therefore, to realize good water availability for productive use and allocate water optimal manner constructing a hydraulic structure (dam) upstream of the watershed was recommended.

Nonlinear Flood Responses to Tide Level and Land Cover Changes in Small Watersheds

Regarding global warming, the threat of flooding is projected to increase due to the change in intensity and frequency of single drivers and amplification caused by multi-driver interactions. This interaction becomes more complicated in developing regions with rapidly changing land cover. As a result, demands on flood risk management are rising especially in small watersheds, which are more vulnerable to driver disturbances compared with large watersheds. Existing studies focused on large watersheds rather than small watersheds. However, the findings derived from large-scale analysis cannot be transferred to small watersheds directly. This research investigated the flood responses in the Yonghe River Watershed (YRW) (63.8 km2) in Guangzhou, China, considering the impact of land cover change. The YRW experienced a disastrous compound flood on 22 May 2020. A hydrodynamic model integrating the Hydrologic Engineering Center’s Hydrologic Modeling System and River Analysis System (HEC-HMS and HEC-RAS, respectively) was established and calibrated using the inundation depths observed during the flood. Model analysis using multiple scenarios showed that the watershed is river-dominated, and flood responses to the three factors are nonlinear but with different increasing rates. The response curves for tide levels and land cover changes increase faster at high values, whereas the rainfall intensity curves vary slightly. These findings highlight the importance of integrating tidal impacts into flood risk management, even in river-dominated coastal watersheds. The study further recommends that in small watersheds, 50% imperviousness is an indicator of the urgent demand for flood risk management measures.

River ice modelling for hydropower operations at Albeni Falls Dam, Idaho

This paper presents a method to quantitatively assess the impact of ice-affected hydraulics on hydropower operations using the widely used Hydrologic Engineering Center River Analysis System (HEC-RAS) hydraulic model. The model was used to identify historical periods where downstream river ice growth reduced the net head on a US Army Corps of Engineers dam on the Pend Oreille River in Idaho, USA. The model was used to create curves of projected stage impacts based on border-ice and continuous bank-to-bank ice formation scenarios. The temperature modelling capabilities in HEC-RAS were used to hindcast ice formation periods on the river that were validated with shore-based webcam data. These results provide a framework that can be applied to other rivers with hydropower generation, particularly sites where an open-water HEC-RAS model exists.

Analysing the Impact of Land Subsidence on the Flooding Risk: Evaluation Through InSAR and Modelling

Floods greatly impact human settlements in flood risk areas, such as floodplains and coastal lowlands, following heavy rainfall. The Alto Guadalentin valley, an orogenic tectonic depression, experiences extreme flash floods and land subsidence due to groundwater withdrawal, rendering it one of Europe's fastest subsiding regions. In this study, we compared two 2D flood event models representing different land subsidence scenarios for 1992 and 2016. To determine the flooded area and water depth variations due to land subsidence, the Hydrologic Engineering Centre River Analysis System 2D (HEC-RAS 2D) model was used to simulate flood inundation by the Alto Guadalentin River and its tributaries. Synthetic aperture radar (SAR) satellite (ERS, ENVISAT, and Cosmo-SkyMED) images were employed, along with the interferometric SAR (InSAR) technique, to calculate the magnitude and spatial distribution of land subsidence. By analysing the accumulated subsidence distributions obtained from InSAR, the original topography of the valley in 1992 and 2016 was reconstructed. These digital surface models (DSMs) were then used to generate 2D hydraulic models, simulating flood scenarios in the unsteady mode. The results demonstrated significant changes in the water surface elevation over the 14-year period, with a 2.04 km2 increase in areas with water depths exceeding 0.7 m. These findings were utilized to create a flood risk map and assess the economic flood risk. The data highlight the crucial role of land subsidence in determining the inundation risk in the Alto Guadalentin valley, providing valuable insights for emergency management and civil protection against future potential flooding events.

Transport Analysis of Copper in Boac River Marinduque, Philippines

In 1996, over 1.6 million cubic meters of mine tailings were released along Boac River, Marinduque, Philippines declaring it biologically dead. Numerous studies found traces of heavy metals along this river, and bioaccumulation became a problem. Several heavy metals contaminating the river were reported to be ingestible by humans; one of which is Copper (Cu). With that, this study investigated Cu dispersion in the downstream area of Boac River. This was first done by coordinating with the “Development of Health Index: Vulnerability to Extreme Environmental Events for Marinduque island.” (D-HIVE) project in collecting the water quality data, rainfall data, and digital elevation models (DEMs). Using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) 6.2, the analyses were conducted by inputting the collected data. The flood hazard maps showed minimal spread in high-elevation areas and distinct spread in low-lying areas. The highest flood velocity was in the starting and middle points of the watershed, indicating these areas have the highest potential to liftCu content and transport it into the downstream area and floodplain. The spatial variability maps showed that pH increases while Cu content decreases as water flows toward the downstream area. Overall, this study successfully determined the dispersion of Cu along Boac River.

Application of geospatial technique and hydrological modelling in evaluating reservoir sedimentation in Chhattisgarh state of India

ABSTRACT Soil erosion and sediment deposition in the reservoirs are both consequences of the change in precipitation pattern that has been observed over the past decade. For the purpose of storing water in reservoirs built before the 1980s, the Indian state of Chhattisgarh has a few irrigation projects. As the state has grown, agricultural land has increased while the proportion of forest area has decreased. Due to this development, runoff is introducing more sediment into the reservoirs, resulting in a reduction in intended storage. The effects of sediment deposition in the Kodar reservoir, located in the Mahasamund district of Chhattisgarh State, are evaluated in present study. Prior to five years, the reservoir reported a 30% deficit in actual irrigation. To quantify the sediment load, Remote Sensing (RS), Geographic Information System (GIS), and the Hydrologic Engineering Centre – Hydrologic Modelling System (HEC-HMS) were effectively employed. From 2012 to 2014, the reservoir received an average sediment load of 1.11 million tonnes. In order to preserve soil in the watershed’s erosion-prone area, a treatment plan is developed. It includes numerous thematic maps that are overlaid in order to provide preventive measures against reservoir sedimentation and is further proposed in the form of best management practices.

An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States

Abstract. Floods are among the most destructive natural hazards in the world, posing numerous risks to societies and economies globally. Accurately understanding and modeling floods driven by extreme rainfall events has long been a challenging task in the domains of hydrologic science and engineering. Unusual catchment responses to flooding cause great difficulty in predicting the variability and magnitude of floods, as well as proposing solutions to manage large volumes of overland flow. The usage of nature-based solutions (NBSs) has proved to be effective in the mitigation of flood peak rate and volume in urban or coastal areas, yet it is still not widely implemented due to limited knowledge and testing compared to traditional engineering solutions. This research examined an integrated hydrological and hydraulic modeling system to understand the response of an at-risk watershed system to flooding and evaluate the efficacy of NBS measures. Using the Hydrologic Engineering Center Hydrologic Modeling System and River Analysis System (HEC-HMS and HEC-RAS) software, an integrated hydrologic–hydraulic model was developed for Hurricane Matthew- (2016) and Florence-driven (2018) floods across the Little Pee Dee–Lumber River watershed, North and South Carolina (the Carolinas), US. The focus was on Nichols, a small town that has disproportionately been impacted by flooding during these two hurricane events. The present article proposes a methodology for selecting, modeling, and evaluating the performance of NBS measures within a catchment, which can be extended to other case studies. Different NBS measures, including flood storage ponds, riparian reforestation, and afforestation in croplands, were designed, modeled, and evaluated. Hurricane Matthew's flooding event was used for evaluating the NBS scenarios given its high simulation accuracy in flood inundation compared to the less accurate results obtained for Hurricane Florence. The scenario comparison evidenced that large-scale natural interventions, such as afforestation in croplands, can reduce the inundated area in Nichols by 8 % to 18 %. On the contrary, the smaller-scale interventions such as riparian reforestation and flood storage ponds showed a negligible effect of only 1 % on flood mitigation.

Leveraging mesh modularization to lower the computational cost of localized updates to regional 2D hydrodynamic model outputs

Hydrodynamic model outputs are used in urban flood risk modelling, flood alert systems, and Monte Carlo hazard assessment. This study tackles an under-explored challenge wherein regular updates to the spatial characteristics of the watershed – due to factors such as changing land use – alter the watershed’s response to rainfall forcing, thus rendering existing model outputs obsolete. Because state-of-the-art hydrodynamic models are computationally expensive, frequently re-running simulations can be costly. Modularization addresses this problem by requiring re-computation only for a limited domain affected by the land use changes. This article introduces a novel approach by modularizing the 2D domain into independent sub-domains before (‘discrete’) and after (‘abstract’) the numerical computations. Using the Hydrologic Engineering Center River Analysis System (HEC-RAS) 2D model of a large urban watershed in Houston as an illustrative and generalizable testbed, we show that both the discrete and abstract modularization closely approximates the results from re-running the entire model. The computational cost of modularization scales linearly with model size for memory requirements as storing the solution on the interior boundaries (discrete) or throughout the domain (abstract) are necessary. This trade-off of memory for computation may facilitate advances in surrogate modelling or Monte Carlo flood risk assessment.

Rainfall-Runoff Modelling Using HEC-HMS Model, Remote Sensing and GIS in Middle Gujarat, India

Hydrological modeling is a widely used approach for estimating the hydrological response of a basin to precipitation. Floods are among the most catastrophic natural disasters in small urban watersheds, inflicting loss of life, massive property destruction, and a severe danger to the economy. As a result, appropriate modeling can be a useful tool in preventing and mitigating such flood hazards. Despite this, flash flood prediction remains one of the challenges of hydrological modeling in ungauged basins due to a lack of runoff observations. This study aims to calibrate and validate the rainfall-runoff transformation model for Hathmati river sub watershed in the Sabarmati River basin using HEC-HMS (Hydrologic Engineering Centre Hydrology Modeling System). For the loss rate, SCS Curve Number method was selected while Clark Unit Hydrograph and SCS unit hydrograph was used for the transform method. The model is calibrated and verified using two rainfall-runoff events from 2006 and 2007 year The model calibration and validation efficiency were verified for both methods using the Nash-Sutcliffe efficiency (NSE), The coefficient of determination (R2), and the Percent Bias (PBIAS) As a result, the model calibration and validation were found to be satisfactory with the acceptable value of NSE between 0.869 to 0.914, with R2 0.901 to 0.947 and PBIAS from 9.76 to 14.8. it is observed that the model shows a very good correlation between simulated flow and observed flow. As a result, the model can be used to forecast river flow and aid in flood mitigation efforts to lessen their effects and associated costs. Additionally, the findings of this study can serve as guidelines for future assessments of the flood risk in the study area.

Evaluation of post extreme floods in high mountain region: A case study of the Melamchi flood 2021 ​at the Koshi River Basin in Nepal

Extreme precipitation events and associated hazards are becoming more frequent and intense due to climate change. On June 2021, a devastating flash flood occurred in the Melamchi River in Nepal causing immense destruction to lives and property. The flood event was cascading in nature and associated with multiple driving factors. This study evaluates the post-extreme flood conditions of the Melamchi Flood 2021 (MF21). Rainfall, streamflow (discharge), and gauge height (water level) data of the nearest stations were analyzed. Since there was a lack of reliable instantaneous river discharge during the disastrous MF21, this study developed the flow rating curves at hydrometric stations. Post-extreme flood was evaluated considering upstream catchment hydrology, glacial lake outbursts, landslide damming, and the hydrodynamic propagations using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model and gauge-to-gauge (G2G) correlation. The results were then compared to field observations. The HEC-RAS model was simulated for several flood frequency periods in different return periods (namely 2, 5, 10, 25, 50, 100, 200, 500, and 1000 years). Results showed that the average 24 ​h gauge height and discharge at Nakote station (a few km downstream of landslide damming) are 15.88 ​m and 169.18 ​m3/s, in which the model-based highest frequency of 1-h extreme flood and return period can reach 716 ​m3/s in 1000 years. During MF21, the discharge of the Nakote region at the 10 ​min intervals was estimated to be with a maximum of 7162.10 ​m3/s, an average of 2207.13 ​m3/s and a minimum of 237.44 ​m3/s.Additionally, the outburst discharge from glacial lake at high elevation was estimated to be 295.14 ​m3/s contributing to downstream flooding. The model-based discharge during the past flood period was also estimated in 9 cross-section points of the river in which discharge at the damming area was 63.81 ​m3/s and downstream Dumredovan was 1206.58 ​m3/s. This research provides valuable insights to post-extreme flood evaluation and the approach can be used for other watersheds in evaluating post flood conditions.

Flood-susceptibility-based building risk under climate change, Hyderabad, India

Abstract Urban floods have been highly prominent natural disasters occurring in catchments across the globe, causing financial loss and damage to buildings. This necessitates effective and sustainable mitigation mechanisms. In this context, flood-susceptibility-based building risk (FSBR), a combined index for evaluating flood susceptibility and building risk simultaneously to understand the impact of the flood, is proposed by fusing XGBoost (facilitates flood susceptibility) and Hydrologic Engineering Center River Analysis System 2D (enables building risk) in climate change situations. The methodology is applied to Greater Hyderabad Municipal Corporation, India. Six combinations of FSBR, namely, high building risk and high flood susceptibility (HH), high and medium (HM), medium and medium (MM), medium and high (MH), low and medium (LM), and low and high (LH) are employed to study the urban floods. The total affected areas for HH, HM, MH, MM, LH, and LM are 63.40 km2 (52.627%), 28.92 km2 (24%), 9.52 km2 (7.9%), 4.81 km2 (3.99%), 9.26 km2 (7.686%), and 4.56 km2 (3.79%) (totalling 120.47 km2). The number of corresponding buildings is 182,178, 84,136, 46,238, 22,691, 48,092, and 23,781. Waterproofing as a mitigation measure is considered. The total cost of waterproofing is Rs 4,964.60 cr.

A Modified SCS Curve Number Method for Temporally Varying Rainfall Excess Simulation

The SCS curve number (SCS-CN) method has gained widespread popularity for simulating rainfall excess in various rainfall events due to its simplicity and practicality. However, it possesses inherent structural issues that limit its performance in accurately simulating rainfall excess and infiltration over time. The objective of this study was to develop a modified CN method with temporally varying rainfall intensity (MCN-TVR) by combining a soil moisture accounting (SMA) based SCS-CN method with the SMA method in the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS). In the MCN-TVR, the SMA-based SCS-CN method is utilized to simulate the cumulative rainfall excess and infiltration, while the SMA method in the HEC-HMS serves as an infiltration control function. A key advantage of the MCN-TVR is that it eliminates the need for additional input parameters by inherently linking the parameters in the two SMA-based methods. Sixteen hypothetical 24 h SCS Type II rainfall events with different soil types and five real rainfall events for the Rush River Watershed in North Dakota were used to assess the performances of the MCN-TVR method and the SMA-based SCS-CN method. In the hypothetical simulations, the rainfall excess simulated by the SMA-based SCS-CN and MCN-TVR models was compared to that simulated by a Green–Ampt model. Discrepancies were observed between the rainfall excess simulated by the SMA-based SCS-CN and Green–Ampt models, especially for coarse soils under relatively light rainfall. However, the MCN-TVR model, incorporating an infiltration control function, demonstrated its improved performance closer to the Green–Ampt model. For all the hypothetical events, the Nash–Sutcliffe efficiency (NSE) coefficient of the rainfall excess simulated by the MCN-TVR method compared to the Green–Ampt model was greater than 0.99, while the root mean standard deviation ratio (RSR) was less than 0.03. In the real applications, the SMA-based SCS-CN model failed to provide acceptable simulation of the direct runoff for rainfall events with durations of less than the time of concentration. In contrast, the MCN-TVR model successfully simulated the direct runoff for all the events with NSE values ranging from 0.65 to 0.91 and RSR values from 0.31 to 0.56.

Assessment of Manning coefficient for Dujila Canal, Wasit/-Iraq

Abstract Assessment of Manning’s unevenness factor is of critical significance in hydraulic readings of open channel stream. Manning’s ( n n ) is obligatory for the computation of releases, water levels, and backwater curves, as well as plan curve additions. The correct value of Manning’s roughness coefficient ( n n ), i. H. a value that accurately reflects the observed data, is selected by the calibration process. The current study aims to calculate the Manning coefficient of the Dujila Canal due to this factor helps in developing water management systems, the Dujila Canal (a branch of the Tigris River) was modeled using the Hydrologic Engineering Center River Analysis System steady-flow model to guess the rate of Manning’s factor. The information was reserved for seven cross sections for calibration, then another cross section is applied for the verification process. A statistical test on the recorded data must be used to verify the calibration process. It uses the Root Mean Square (RMS) test. The outcomes show that the appropriate Manning’s ( n n ) rate was (0.025), providing a reasonable arrangement amid the designed and perceived sketches

Assessment of impact of climate change on the streamflow of Idamalayar River Basin, Kerala

Abstract This study investigates the impacts of climate change on water availability in the Idamalayar basin, Kerala. Soil and Water Assessment Tool (SWAT), Hydrologic Engineering Centre – Hydrologic Modelling System (HEC-HMS), and bias-corrected climate change data were used to simulate future streamflows. The performances of SWAT and HEC-HMS were assessed using four statistical indices (R2, Nash–Sutcliffe efficiency, percentage bias, and RSR). SWAT slightly outperformed HEC-HMS. The CMIP6 general circulation models (GCMs) were selected using PROMETHEE-2. The variations in climate variables and streamflows were studied for three future periods, i.e., near-future (2031–2040), mid-future (2051–2060), and far-future (2071–2080) under three shared socio-economic pathway (SSP) scenarios (SSP126, SSP245, and SSP585). The projections of GCMs show different patterns in variation of precipitation. Generally, there is a slight increase in annual precipitation. However, there was a notable decline in peak in July. An additional peak was often seen in October. The maximum and minimum temperatures showed a decreasing trend. The average annual streamflow reduction under SSP126 was approximately 25.63, 27.92, and 26.24% in the near, mid, and far future, respectively. Under SSP245, the average decrease was 30.71, 16.06, and 19.06% for the near, mid and far future, respectively. For SSP585, there was 12.73% increase in the far-future period.

Wavelet Analysis and the Information Cost Function Index for Selection of Calibration Events for Flood Simulation

Globally, floods are a prevalent type of natural disaster. Simulating floods is a critical component in the successful implementation of flood management and mitigation strategies within a river basin or catchment area. Selecting appropriate calibration data to establish a reliable hydrological model is of great importance for flood simulation. Usually, hydrologists select the number of flood events used for calibration depending on the catchment size. Currently, there is no numerical index to help hydrologists quantitatively select flood events for calibrating the hydrological models. The question is, what is the necessary and sufficient amount (e.g., 10 events) of calibration flood events that must be selected? This study analyses the spectral characteristics of flood data in Sequences before model calibration. The absolute best set of calibration data is selected using an entropy-like function called the information cost function (ICF), which is calculated from the discrete wavelet transform (DWT) decomposition results. Given that the validation flood events have already been identified, we presume that the greater the similarity between the calibration dataset and the validation dataset, the higher the performance of the hydrological model should be after calibration. The calibration datasets for the Tunxi catchment in southeast China were derived from 21 hourly flood events, and the calibration datasets were generated by arranging 14 flood events in sequences from 3 to 14 (i.e., a Sequence of 3 with 12 sets (set 1 = flood events 1, 2, 3; set 2 = flood events 2, 3, 4, …, and so on)), resulting in a total of 12 sequences and 78 sets. With a predetermined validation set of 7 flood events and the hydrological model chosen as the Hydrologic Engineering Center (HEC–HMS) model, the absolute best calibration flood set was selected. The best set from the Sequence of 10 (set 4 = S10′) was found to be the absolute best calibration set of flood events. The potential of the percentile energy entropy was also analyzed for the best calibration sets, but the ICF was the most consistent index to reveal the ranking based on similarity with model performance. The proposed ICF index in this study is helpful for hydrologists to use data efficiently with more hydrological data obtained in the new era of big data. This study also demonstrates the possibility of improving the effectiveness of utilizing calibration data, particularly in catchments with limited data.

Flood Modeling using Hydrologic Engineering Center's - Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) for a Micro Watershed in Castilla, Sorsogon

Flood Modeling using Hydrologic Engineering Center's - Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) for a Micro Watershed in Castilla, Sorsogon

Flood Modeling using Hydrologic Engineering Center's - Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) for a Micro Watershed in Castilla, Sorsogon

Flood Modeling using Hydrologic Engineering Center's - Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) for a Micro Watershed in Castilla, Sorsogon

Study effect of slope change on sediment transport in the Dujila irrigation canal


 One of the main elements influencing the Dujila River's river shape is sediment. The characteristics of sediment supplied within reach when creating hydraulic constructions like a dam, irrigation canal, maybe even to use the stream for navigation purposes, would be one of the design engineer's main concerns.. The shallow depth brought on by riverbed aggradation may have an impact on the Dujila River's utilization as a source of drinking water and agricultural irrigation. Investigating sediment movement and the features of the river bed is essential to reducing the problems caused by River morphology changes. Utilizing the River Analysis System of the Hydrologic Engineering Center, a hydraulic examination of the transfer of sediment model was produced for this project (HEC-RAS). Along the Dujaili's 57 kilometers Information was gathered. The Manning coefficient was calculated for seven sections after calibration and verification. For 6 sections, samples from the river bed and suspended loads were taken in order to calculate the amount of silt. The model's output displays the pattern of river reach sections that follow erosion or deposition as well as the pattern of bed change. The model has been tested with various slope values to determine how slope affects sediment transport.

Flood propagation modeling: Case study the Grand Ethiopian Renaissance dam failure

Despite significant improvements in design methodologies, dams and water-retaining structures failures continue to occur. Dams’ failure analysis plays a crucial role in the development of dam safety planning and emergency action. Since the Grand Ethiopian Renaissance Dam (GERD) has been built, there have been many concerns about its safety and its effects on downstream countries in case of its failure. In this paper, the GERD break was modeled by using the USACE Hydrologic Engineering Center’s River Analysis System (HEC-RAS). Two dimensions model under different failure scenarios was suggested. Moreover, outflow hydrographs and flood inundation maps were presented due to dam breach. Finally, it was concluded that, in case of catastrophic failure, flow depths may vary from 3 to 10 m in some residential areas, such as Khartoum. Furthermore, the water surface elevation may reach 184 m above msl (mean sea level) in Lake Nasser in case of dam failure with a fully stored GERD reservoir. Moreover, the maximum flow would reach 325,928 m3/sec which is more than 21.5 times the total capacity of the dam spillway. Finally, the paper’s outcomes may assist decision-makers in developing alternate plans to deal with the dangers of GERD break.

Performance of the flood models in different topographies

Climate change leads to more severe floods, which needs to be modeled properly. This study uses two hydraulic models including Hydrologic Engineering Center River Analysis System (HEC-RAS) and LISFLOOD-FP to simulate floods in two basins (Kashkan and Missouri) with different topographies (mountain and plain terrain). The impacts of different parameters like input resolutions, Digital Elevation Models (DEMs), roughness coefficient, and modeling techniques on flood simulation are examined. Results indicate that the performance of the models varies based on topography, input resolutions, and modeling approach. Indeed, the 2D modeling of both models in different topography types leads to better results compared to 1D/2D modeling. In Kashkan basin, HEC-RAS and LISFLOOD-FP models simulate floodplain flow with accuracies of 74% and 70%, respectively, and the accuracies of the models in Missouri basin are 80% and 84%, respectively. Although both models, particularly HEC-RAS with ALOS 30 m, can provide reasonable results, with an accuracy of 74% for Kashkan and 63% for Missouri basin, LISFLOOD-FP can perform better (about 4% higher accuracy) than HEC-RAS with a higher resolution of DEM (3DEP) in Missouri basin with significantly lower computational costs. In addition, the performance of the two models significantly decreases using ASTER DEM in plain topography. Results confirm that HEC-RAS outperforms LISFLOOD-FP in coarser resolution DEMs, while LISFLOOD-FP performs better in plain topography, which requires higher resolution DEMs. Despite the low sensitivity of the two models to roughness coefficients, the variability of the LISFLOOD-FP simulations is smaller than HEC-RAS. This work uses the best setting and factors to simulate floodplain zones, which is valuable for flood risk and hazard assessment to reduce and manage damages and losses.