Assessing the Impact of Climate Change on Flood Events Using HEC-HMS and CMIP5

In the context of changing climate conditions, the design of hydrographs faces increasing uncertainties due to shifts in precipitation patterns, hydrological regimes, and a rise in extreme weather events. This study assesses potential uncertainties in design hydrographs linked to future climate change in the Kalu River Basin, Sri Lanka, focusing on the Ellagawa sub-basin. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was selected based on a comprehensive literature review to account for anticipated changes in rainfall patterns and their impact on streamflow. Seven precipitation gauging stations (Alupolla, Balangoda, Galatura, Halwathura, Pussella, Ratnapura, and Wellandura) were chosen following World Meteorological Organization (WMO) guidelines, based on data availability and percentages of missing data. Streamflow data for Ellagawa and Ratnapura stations were obtained from the Sri Lankan Irrigation Department, with daily precipitation and streamflow data from 1980 to 2017 used for analysis. The model was calibrated and validated using data from four extreme events identified through frequency analysis, each associated with daily precipitation levels corresponding to a 100-year return period. Future changes in precipitation extremes were evaluated using outputs from three General Circulation Models (GCMs): CNRM-CM6-1, HadGEM3-GC31-LL, and MRI-ESM2-0, under two Shared Socioeconomic Pathways (SSP2 and SSP5) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), downscaled to local scale, focusing on the period from 2081-2100. The annual maximum daily precipitation for both observed and projected scenarios was analyzed using Generalized Extreme Value (GEV), Weibull, and Gamma distribution functions. The Nash-Sutcliffe efficiency (NSE) coefficients, ranging from 0.79 to 0.85 during calibration and validation, indicated a close match between simulated and observed river flows. Different GCMs and SSPs predicted varying changes in rainfall regimes and design hydrographs. Specifically, factors such as the frequency and intensity of extreme precipitation events, changes in the seasonal distribution of rainfall, and prolonged dry spells were identified as critical drivers affecting peak flow in the future. Compared to the baseline period (1980-2017), annual total rainfall is projected to increase by -8% to 40% under SSP2-4.5 and -10% to 36% under SSP5-8.5. The maximum daily precipitation is expected to rise from 79 mm to 139 mm under SSP2-4.5 and from 82 mm to 138 mm under SSP5-8.5. Consequently, the peak flow of the design hydrograph may increase by 3% to 106%. These findings underscore the importance of considering climate change uncertainties in hydrological and hydraulic design. By integrating future climate projections into design processes, engineers and policymakers can better adapt infrastructure and planning to evolve conditions, enhancing resilience and sustainability in water management systems.

Hydrological models are an effective tool for the estimation of peak floods and runoff in planning water development and flood mitigation/adaptation. Continuous hydrologic modeling can determine the relationships between hydrologic processes and environmental changes over long time periods. Therefore, the selection of a theoretically robust and functionally reliable hydrological model is crucial for the effective management of flood risk within a basin. The Soil and Water Assessment Tool (SWAT) is one of the most widely used hydrological models for assessing the impacts of climate change on discharge in large basins. In contrast, the Hydrologic Engineering Centers’ Hydrologic Modeling System (HEC-HMS) has been one of the most rapidly evolving and promising hydrological models, with its latest version (v4.9) supporting both fully distributed and semidistributed hydrological modeling approaches. This study was conducted to compare the performances of SWAT and HEC-HMS in long-term continuous simulations in the Pearl River Basin, which is the second largest river basin in terms of discharge in China. For purposes of comparison, both models employed identical input data and comparable configurations. The results demonstrate that both models are capable of predicting river discharge at designated station satisfactories, with the Nash-Sutciffe coefficient exceeding 0.7. Benefiting from its elaborate use of the modified Soil Conservation Service (SCS) loss model and more advanced automatic calibration program, SWAT obtained more accurate results than HEC-HMS in the validation period. However, HEC-HMS is distinguished by its customizable options for constructing hydrological models, and it exhibits considerable potential for application in large-scale river basins such as the Pearl River Basin, enabling long-term, continuous hydrological simulations.

Investigation on historical flood events in river catchment requires a proper estimation of discharge, that can achieve by developing hydrological modelling of the catchment. However, understanding the complex relationships between rainfall and runoff process is a complex task, and hydrologic models should be well-calibrated to make the application of the model effective. In this study, Hydrologic Engineering Center-hydrologic modelling system (HEC-HMS) has been developed for several sub-catchments in Kelantan River, Malaysia, for prediction of its hydrologic response and flood events for several return periods. The results are indicating the good performance of the HEC-HMS model for discharge simulation, in which the correlation coefficient (r) has been found to range from 0.64-0.98 and 0.75-0.95 during the calibration and validation periods, respectively. The study also shows that the major floods in the Kelantan River catchment have been in line with the 50-year ARI design flood that been simulated by the model. The proposed HEC-HMS model could be beneficial to the studied catchment in terms of preparing a comprehensive guideline of a risk for the existing and future flood problems.

To reproduce historical stream flows, climate and land-use change studies require watershed models with physically based parameters, rather than empirical models that are simply calibrated. With this in mind, soil moisture accounting and the temperature index (degree-day) snowmelt models embodied in the Hydrologic Engineering Center's hydrologic modeling system (HEC-HMS) are applied to three Great Lakes watersheds—Kalamazoo, Maumee, and St. Louis—with different climatic and land-use characteristics. Watershed and subwatershed models are calibrated and validated on a daily time step using gauge precipitation measurements, observed snow water equiv- alent data, and physically based parameters estimated using geospatial databases. Results are compared with area-scaled outputs from the National Oceanic and Atmospheric Administration (NOAA) large basin runoff model (LBRM) for historical conditions. The results show modest improvements resulting from the increased spatial resolution of the HEC-HMS models, in addition to the benefits of the more process- based snow algorithm in HEC-HMS, particularly for the snow-dominated St. Louis watershed. However, both LBRM and HEC-HMS models had difficulty reproducing peaks in late winter and early spring runoff, and discrepancies could not be attributed to any systematic errors in the snowmelt models. DOI: 10.1061/(ASCE)HE.1943-5584.0000591. © 2013 American Society of Civil Engineers. CE Database subject headings: Hydrologic models; Snow; Calibration; Runoff; Great Lakes; Watersheds. Author keywords: Hydrologic modeling; Snow modeling; Calibration; Hydrologic Engineering Center's hydrologic modeling system (HEC-HMS); Snow water equivalent; Large basin runoff model; Great Lakes; Runoff.

Accurate river discharge prediction is crucial for effective water resources management, flood forecasting, and disaster preparedness. While physically based hydrological models such as the Hydrologic Engineering Centre - Hydrologic Modelling System (HEC-HMS) are widely used because they explicitly represent catchment processes, they often require extensive datasets, involve complex parameterisation, and demand intensive calibration, which limits their applicability in data-scarce regions. In contrast, Machine Learning (ML) techniques provide a data-driven alternative capable of capturing complex, non-linear relationships of the rainfallrunoff process without explicit physical assumptions. This research presents a comparative analysis of HEC-HMS and four ML algorithms (Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks, Random Forest (RF), and XGBoost), for streamflow prediction in two contrasting Sri Lankan River basins: the floodprone Kelani River Basin in the wet zone and the irrigation-influenced Malwathu Oya Basin in the dry zone. Daily precipitation and discharge data (1990-2020 for Kelani; 2006-2018 for Malwathu Oya) were used for model calibration and validation. HEC-HMS was configured with the Soil Moisture Accounting, Clark Unit Hydrograph, and Muskingum routing methods, whereas ML models utilised rainfall and lagged discharge as input variables. Model performance was evaluated using multiple statistical indicators, including Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), Root Mean Square Error (RMSE), Percent Bias (PBIAS), and R². Results indicate that ML models consistently outperformed HEC-HMS in both basins. In the Kelani basin, LSTM achieved the highest accuracy (NSE = 0.89, KGE = 0.90), effectively reproducing flood peaks, whereas HEC-HMS exhibited moderate performance (NSE = 0.81). In the Malwathu Oya basin, LSTM and XGBoost performed similarly well (NSE ≈ 0.80), successfully capturing irrigation-driven variation and low-flow conditions, while HEC-HMS showed weaker performance (NSE = 0.49, PBIAS = 15.82). The findings highlight the adaptability of ML methods, particularly LSTM, in capturing diverse hydrological regimes, while showing the value of process-based models like HEC-HMS in understanding physical mechanisms. The study recommends developing hybrid modeling frameworks that combine process-based strengths of HEC-HMS with the predictive power of ML to improve flood forecasting and water management in tropical, data-scarce regions.

Application of HEC-HMS for flood forecasting in Misai and Wan'an catchments in China

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

This research aims to map flood inundated areas under changing climate in the Boyo watershed of Southern Ethiopia. A semi-distributed physically based Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center-River Analysis System (HEC-RAS) were used to simulate the flood events and maps, respectively, for climate scenarios. The bias-corrected data of four climate models were used for the baseline (1976–2005), mid-term (2041–2070) and long-term (2071–2100) cycles under RCP4.5 and RCP8.5 scenarios. The 50- and 100-year return period flood events were generated from the baseline and future period streamflow data. The HEC-RAS model was used to simulate the inundation areas and depths from the flood events. The result exhibited that the average annual rainfall and maximum and minimum temperatures of the catchment will increase in the future with an increase in annual runoff. The severity of annual floods would increase in the future under RCP4.5 and RCP8.5 scenarios. Approximately, 193 ha of the study may be flooded with flood events having a return period of 100 years under the RCP8.5 scenario in the long-term period, which is an extreme case. The result is a benchmark to reduce the flood risk and management of floodplains in this watershed.

Abstract. The atmospheric–hydrological coupling systems are essential to flood forecasting because they allow for more improved and comprehensive prediction of flood events with an extended forecast lead time. Achieving this goal requires a reliable hydrological model system that enhances both rainfall predictions and hydrological forecasts. This study evaluates the potential of coupling the mesoscale numerical weather prediction model, i.e., the weather research and forecasting (WRF) model, with different hydrological modeling systems to improve the accuracy of flood simulation. The fully distributed WRF-Hydro modeling system and the semi-distributed Hydrological Engineering Center Hydrological Modeling System (HEC-HMS) were coupled with the WRF model, and the lumped HEC-HMS model was also adopted using the observed gauge precipitation as a benchmark to test the model uncertainty. Four distinct storm events from two mountainous catchments in northern China characterized by varying spatial and temporal rainfall patterns were selected as case studies. Comparative analyses of the simulated flooding processes were carried out to evaluate and compare the performance of the coupled systems with different complexities. The coupled WRF–HEC-HMS system performed better for long-duration storm events and obtained optimal performance for storm events uniformly distributed both temporally and spatially, as it adapted to more rapid recession processes of floods. However, the coupled WRF–HEC-HMS system did not adequately capture the magnitude of the storm events as it had a larger flow peak error. On the other hand, the fully distributed WRF–WRF-Hydro system performed better for shorter-duration floods with higher flow peaks as it can adapt to the simulation of flash floods. However, the performance of the system became poor as uniformity decreased. The performance of the lumped HEC-HMS indicates some source of uncertainty in the hydrological model when compared with the coupled WRF–HEC-HMS system, but a larger magnitude error was found in the WRF output rainfall. The results of this study can help establish an adaptive atmospheric–hydrologic coupling system to improve flood forecasting for different watersheds and climatic characteristics.

The application of two-dimensional (2D) hydrologic and hydraulic modeling tools is increasing for overland flow simulation, as they represent spatial changes in depth, velocity, and flow conditions more accurately. Recently, the US Army Corps HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) added the capability to import an unstructured 2D mesh, which enables the routing of excess precipitation across the mesh, as a fully distributed hydrological model. In HEC-HMS, the 2D diffusion-wave component functions as a hydrologic transform representing overland flow routing. In contrast, HEC-RAS 2D (Hydrologic Engineering Center-River Analysis System), initially applied to river flow simulation, can apply either the 2D shallow-water equations or the 2D diffusion-wave option. Similarly to HEC-HMS, HEC-RAS also includes rain-on-grid (RoG) capability and infiltration algorithms, and in this fashion has some hydrological modeling capabilities. Still, while HEC-HMS is capable of representing extended-period hydrological simulations, HEC-RAS hydrological capabilities are limited to event-based simulations, as there are no provisions to represent abstractions such as evapotranspiration or groundwater/baseflow contributions together. Studies performing a direct comparison between the HEC-HMS RoG and HEC-RAS RoG approaches for representing urban hydrology remain scarce. This study aims to fill that gap by assessing their performance in Moore’s Mill Creek Watershed, in Lee County, Alabama, with a focus on continuous rainfall-runoff modeling. Both models run on the same unstructured mesh and use identical rainfall, terrain, land-use, and soil data. Model simulations are compared over an extended period to evaluate simulated depth, velocity, and flow hydrographs against field observations. The comparison shows HEC-HMS’s superior performance for extended simulation and provides practical guidance on parameter alignment, data needs, and tool selection.

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.

The downstream low lying region of the Kelani River including the Colombo suburbs, experience severe inundation due to heavy rainfalls in the upper basin of the Kelani River. Occurrence of heavy rainfalls is expected to be more frequent in the tropics with the impact of climatic change (IPCC, 2007). Therefore, understanding future rainfall intensity in the river basin and inundation in the low lying region along the lower reach of the Kelani River is extremely important as this is a region with a high population density and economic activities in the suburbs of the capital. The present study analyses the potential extreme rainfalls and resulting flood inundation along the lower Kelani River. Coarse grid atmospheric parameters provided by Global Climate Model (GCM) models for A2 (high emission scenario) and B2 (low emission scenario) scenarios of Intergovernmental Panel on Climate Change (IPCC, 2007) were downscaled to local scale by applying Statistical Downscaling Model (SDSM). Flood discharge and inundation along the Kelani River reach below Hanwella were analyzed by applying two-dimensional flood simulation model (FLO-2D). Inflow to the model at Hanwella, is estimated by the Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) model under future extreme rainfall events. Areas vulnerable to inundation under the above climatic change scenarios are presented.

The Angat Reservoir serves as a major water source for Metro Manila, providing most of the region’s domestic, agricultural, and hydropower needs. However, its dependence on rainfall makes it sensitive to climate variability and future climate change. This study assesses potential long-term impacts of climate change on water availability in the Angat watershed using the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS). Historical rainfall data from 1994 to 2023 and projections under both RCP4.5 (moderate emissions) and RCP8.5 (high emissions) scenarios were analyzed to simulate future hydrologic responses. Results indicate projected reductions in wet-season rainfall and corresponding outflows, with declines of up to 18% under the high-emission scenario. Increased variability during dry-season flows suggests heightened risks of water scarcity. While these projections highlight possible changes in the watershed’s hydrologic regime, the study acknowledges limitations, including assumptions in rainfall downscaling and the absence of direct streamflow observations for model calibration. Overall, the findings underscore the need for further investigation and planning to manage potential climate-related impacts on water resources in Metro Manila.

Auto-calibration of HEC-HMS Model for Historic Flood Event under Rating Curve Uncertainty. Case Study: Allala Watershed, Algeria

Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes between 1985–2014 in the Godavari River Basin using the Hydrologic Engineering Centre-Hydrologic Modeling System(HEC-HMS) model and remote sensing—GIS (geographic information system) techniques. The purpose of this paper is to analyze the dynamics of land-use/land-cover (LULC) changes for the years 1985, 1995, 2005, and 2014 for the Godavari Basin. The findings reveal an increase of 0.64% of built-up land, a decrease of 0.92% in shrubland, and an increase of 0.56% in waterbodies between 1985–2014. The LULC change detection results between the years 1985–2014 indicated a drastic change in the cropland, forest, built-up land, and water bodies among all of the other classes. The urbanization and agricultural activities are the major reasons for the increase of cropland, built-up land, and water bodies, at the expense of decreases in shrubland and forest. The study had an overall classification accuracy of 92% and an overall Kappa coefficient of 0.9. The HEC-HMS model is used to simulate the hydrology of the Godavari Basin. The analyses carried out were mainly focussed on the impact of LULC changes on the streamflow pattern. The surface runoff was simulated for the year 2014 to quantify the changes that have taken place due to changes in LULC. The observed and the simulated peak streamflow was found to be the same i.e., 56,780 m3/s on 9 September 2014. In the validation part, the linear regression method was used to correlate the observed and simulated streamflow data at the prominent gauge station of the Badrachalam outlet for the Godavari River Basin and give a correlation coefficient value of 0.83. It was found that the HEC-HMS model is compatible and works better for the rainfall–runoff modeling, as it takes into account the various parameters that are influencing the process. The hydrological modeling that was carried out using the HEC-HMS model has brought out the significant impact of LULCC on rainfall–runoff at the Pranhita sub-basinscale, indicating the model’s ability to successfully accommodate all of the environmental and landscape variables. The study indicates that deforestation at the cost of urbanization and cropland expansions leads to decreases in the overall evapotranspiration (ET) and infiltration, with an increase in runoff. The results of the study show that the integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can solve hydrological problems in a river basin.