Engineering Center-Hydrologic Modeling System Research Articles

Analysis and Design of a Box Culvert Using Bentley Culvert Master Software

Box culverts are utilized in situations where natural stream flow intersects with roads and railway lines. This research utilizes a digital elevation model and the water Modeling System software to assess the catchment area of the primary valley and identify the factors contributing to flooding in Qoshtapa City. The study involves an analysis of the existing culvert and generated the necessary data for the design of a new culvert. Despite the presence of the existing culvert, floodwater levels rose to over 1 m above the roadway elevation of Erbil-Kirkuk during the last flood event in 2021–2022. The research collected hydrological and climatic data for the study area, conducted soil type analysis using the Harmonized World Soil Database software, and performed hydraulic calculations to estimate the maximum flood discharge of the valley using the Hydrological Engineering Center-Hydrological Modeling System software for flood return periods of 50, 100, and 200 years, for design, to select the best economic alternative. The new culvert design was executed using Bentley Culvert Master software to ensure that floodwaters can flow through the culvert without rising to street level. The results indicated that the new culvert design surpasses the capacity of the existing one. The results show that the best economic alternative hydraulic design is the first alternative capacity of 201 m3/s of a 100-year return period; the new design cross-section area of the culvert is 52.5 m2.

Rainfall-runoff modelling – a comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS)

ABSTRACT This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon.

Integration of UAV Digital Surface Model and HEC-HMS Hydrological Model System in iRIC Hydrological Simulation—A Case Study of Wu River

This research investigates flood susceptibility in the mid- and downstream areas of Taiwan’s Wu River, historically prone to flooding in central Taiwan. The study integrates the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) for flow simulations with unmanned aerial vehicle (UAV)-derived digital surface models (DSMs) at varying resolutions. Flood simulations, executed through the International River Interface Cooperative (iRIC), assess flood depths using diverse DSM resolutions. Notably, HEC-HMS simulations exhibit commendable Nash–Sutcliffe efficiency (NSE) exceeding 0.88 and a peak flow percentage error (PEPF) below 5%, indicating excellent suitability. In iRIC flood simulations, optimal results emerge with a 2 m resolution UAV-DSM. Furthermore, the study incorporates rainfall data at different recurrence intervals in iRIC flood simulations, presenting an alternative flood modeling approach. This research underscores the efficacy of integrating UAV-DSM into iRIC flood simulations, enabling precise flood depth assessment and risk analysis for flood control management.

Modeling run-off flow hydrographs using remote sensing data: an application to the Bashar basin, Iran

Abstract Precipitation is hard to access in countries like Iran, due to inadequate number of rain gauge stations. Remote sensing provides an alternative source of rainfall estimation. In this study, the effectiveness of the HEC-HMS model was evaluated using GPM (Global Precipitation Measurement Mission) satellite and rain gauge station data. The model was calibrated and validated using 5 flood events' data of a hydrometric station at the outlet of Bashar basin. Important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model, using various statistical indices. The accuracy of QP and V estimations using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was about 1% (9.9% and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1% and 10.2% for GPM and rain gauge data, respectively). Such results imply the sole utilization of satellite precipitation data is reliable for modeling hydrological key parameters, which can be helpful in areas with limited ground station coverage.

Assessing the impact of climate change on surface runoff of the Ubolratana Reservoir, Thailand

Assessing surface runoff in river basins is paramount for effectively managing water resources. Climate change significantly impacts the availability of water within these basins. The Ubolratana reservoir is vital for sustainable water supply, aquaculture, agricultural practices, and domestic needs. This research aims to evaluate the runoff availability of the reservoir by using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) under distinct climate change scenarios. The HEC-HMS model was calibrated based on observed rainfall and runoff data. To project future runoff dynamics, calibrated model parameters were coupled with the bias-corrected rainfall data of Can-ESM5, CESM2, and GFDL-ESM4 models for two Shared Socioeconomic Pathways (SSPs) climate scenarios (SSP 245 and SSP 585). The results show upward trends in both projected rainfall and runoff within the drainage area of the Ubolratana reservoir between 2022 and 2061. In summary, the findings of this research may be useful for regional water resources management and strategic planning endeavors.

Wavelet analysis of rainfall and application of hydrological model in a semi‐arid river basin of Rajasthan, India

AbstractRainfall–runoff modeling requires a selection of a suitable hydrologic model for the determination of an accurate quantity. A better understanding of rainfall–runoff processes is important for stream flow generation within a river basin. This study investigate the long‐term spatial–temporal trend of rainfall and streamflow using the hydrologic engineering center‐hydrologic modeling system (HEC‐HMS) model to understand the rainfall–runoff dynamics in the Banas River basin. Rainfall variability was analyzed for two periods: 1971–1995 and 1996–2020. Continuous wavelet transform was used to analyze the periodicity and the inter‐seasonal relationship in rainfall. HEC‐GeoHMS was used to generate the various inputs for the hydrologic modeling inputs for the HEC‐HMS model. Initial constant loss and ModClark transform method were used to set up the HEC‐HMS model for the study area. Calibration and validation of the model were performed using 3 years of observed data 2010–2012 and 2013–2015, respectively. Statistical model efficiency was checked using the coefficient of determination (R2), Nash–Sutcliffe efficiency, and root mean squared error to evaluate the performance of the HEC‐HMS model. The finding indicated that the R2 values are 0.80 for calibration and 0.82 for validation periods, respectively, which are in good arrangement with the measured values. This study analyzes the relationship between rainfall and runoff, which will aid in proper and efficient water resource management.

Assessing Hydrological Response in the Timah-Tasoh Reservoir Sub-Catchments: Calibration and Validation using the HEC-HMS Model

Hydrological modelling is a tool that is frequently used for assessing the hydrological response of a basin as a result of precipitation. It is also a vital component as water resources and environmental planning management. The study deals with calibrating and validating the hydrological response in the sub-catchments of the Timah-Tasoh reservoir using the hydrological model named Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). This study uses the SCS Curve Number, the SCS Unit Hydrograph, the constant monthly baseflow, and lag routing for the model development. The model was simulated for ten (10) years for calibration and nine (9) years for validation. The model calibration and validation efficiency were assessed using the coefficient of correlation (R). The findings show that the HEC-HMS model performs satisfactorily in simulating the observed daily inflow series, with the R-value of 0.4902-0.5139 during calibration and 0.5047-0.5559 during validation process. Thus, the result obtained from this study can be used as a preliminary development of hydrological modelling of the catchment of the Timah-Tasoh reservoir and can be used for extend application such as water inflow forecasting, impact of land use to the reservoir and others.

Evaluation of a weather forecasting model and HEC-HMS for flood forecasting: case study of Talesh catchment

Reports demonstrate that floods are among the most prevalent and deadliest natural disasters affecting 520 million people annually. The present study seeks to evaluate flood forecasting using the weather research and forecasting (WRF) model and the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model. To this end, WRF and HEC-HMS were calibrated by comparing their results with the data observed at measuring stations. Then, the output rainfall data of the WRF model were implemented by the calibrated HEC-HMS model and were examined using the statistical indices, which were revealed to be 4.13, 3.42, and 2.67 for the flow volume and 6.2, 2.46, and 5.11 for the peak flow, suggesting the accurate performance of WRF model alongside HEC-HMS in the Talesh catchment.

The effect of landuse dynamics on water balance components in the Upper Oum Er Rabia Basin, Morocco

The Upper Oum Er-Rbia Basin (UOERB) has been facing significant land use/land cover change and urban sprawl dynamics in recent years, in addition to significant climatic variability leading to disruption of precipitation intensity and quantity. Our approach is to identify the morphometric characteristics of the study region and monitor changes in land use and land cover by analyzing Landsat images. These factors form the input parameters for the HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) hydrological model for simulating daily historical hydro-climatic events. The classification of Landsat images shows that the Upper Oum Er-Rbia Basin has experienced significant landuse dynamics over the past 30 years, resulting in disrupted rainfall and recurrent flooding events. Overall, the hydrological simulation shows good agreement between observed and simulated flow hydrographs, with Nash criteria between 0.6 and 0.8. This knowledge enables us to better estimate the return flows in the catchment area, and to take appropriate action in terms of hydraulic management and development in the Upper Oum Er-Rbia Basin.

The Alteration of Flood Peak Discharge by Land Cover Change in Prek Thnot Watershed, Kampong Speu Provine, Cambodia

This study analyses the alterations of peak catchment runoff from Prek Thnot watershed using the Soil Conservation Service Curve Number (SCS-CN) methodology. Two major studies using different approaches have previously been conducted in the catchment, albeit producing inconsistent results. The Snowy Mountain Hydro-electric Authority published the first study in 1967 and found that the peak surface flow runoff over a 10-year return period was 710 m3/s. The second study was conducted by the Japanese International Cooperation Agency (JICA) between 1991 and 2006 and reported a peak catchment discharge of 1,380 m3/s. To date, there has been no discussion comparing the two studies to determine why the discharge during the later study was double that of the first. As the annual rainfall during the two study periods was similar, it is suggested that this discrepancy may be attributed to physical changes in the watershed, such as changes to forest cover. We deployed the Hydrologic Engineering Centre-Hydrologic Modeling System to simulate the peak runoff of the catchment. Our result was very similar to the JICA finding. Our data suggested that the increase in catchment discharge may be attributed to the conversion of 26% of forest cover in the catchment to agricultural land since the early 2000s. This information is useful for flood management practices, particularly with respect to managing catchment runoff in the context of rapid deforestation and the hydrological impacts of climate change within the watershed. The paper analyzes the impact of land-use changes on catchment runoff and provides valuable insights for flood management practices in the context of climate change and deforestation.

Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach

The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) to predict the critical overflow level of a small urban stream on Lesvos Island, Greece, which has a history of severe flash flood incidents requiring rapid response. The system is supported by a network of telemetric stations that measure meteorological and hydrometric parameters in real time, with a time step accuracy of 15 min. The collected data are fed into the physical Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), which simulates the stream’s discharge. Considering the HEC-HMS’s estimated outflow and other hydro-meteorological parameters, the EMFS uses long short-term memory (LSTM) neural networks to enhance the accuracy of flood prediction. In particular, LSTMs are employed to analyze the real-time data from the telemetric stations and make multi-step predictions of the critical water level. Hydrological time series data are utilized to train and validate the LSTM models for short-term leading times of 15 min, 30 min, 45 min, and 1 h. By combining the predictions obtained by the HEC-HMS with those of the LSTMs, the EMFS can produce accurate flood forecasts. The results indicate that the proposed methodology yields trustworthy behavior in enhancing the overall resilience of the area against flash floods.

Improving flood forecasting in Narmada river basin using hierarchical clustering and hydrological modelling

The purpose of the study was to use hierarchical clustering and Thiessen polygon algorithms to identify the significant rain gauge stations for flood forecasting at Sardar Sarovar Dam. Rainfall data from 2010 to 2018 was utilized to analyze the catchment region between Omkareshwar Dam and Sardar Sarovar Dam. The study identified two clusters of rain gauge stations with similar rainfall patterns and divided the study area into seventeen regions using Thiessen polygons. The land use map showed that the study area was mostly covered by crop lands, and the soil map divided the area into three types of sedimentary claystone soil. A hydrological model, Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS), was used for rainfall-runoff modeling, and the computed runoff was compared with observed gauge discharge and inflow of Sardar Sarovar Dam. Regression analysis was performed to assess the performance of the model, and the results showed a good correlation between observed rainfall and estimated runoff values for 2012 and 2016. The study concludes that the existing rain gauge network is sufficient for flood forecasting, and the developed model along with the Thiessen polygon method can provide more accurate predictions of flow. The study highlights the importance of selecting suitable rain gauge stations for reliable flood forecasting in flood-prone basins. The study findings can be useful for future runoff prediction and flood forecasting in the study area.

HMS-REST v1.0: A plugin for the HEC-HMS model to provide RESTful services

Web technologies facilitating location-independent participation and broad knowledge sharing are critically important for open science and participative management and decision-making. Therefore, it is crucial to enable hydrological models to run in a web environment. In this study, a representational state transfer (REST) interface is proposed for the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The web service interface (named HMS-REST) was built based on the Java API and Jython script system of HEC-HMS. HMS-REST allows users to retrieve and write model inputs and outputs and perform model execution. These functionalities were evaluated via a case study of pseudoensemble flood forecasting using a data set of six historical flood events from the Chuanchang watershed, southeastern China. Satisfactory performance was achieved in pseudoensemble flood forecasting in terms of the peak flow, total flood volume, peak flow timing and overall hydrograph fitting. This case study indicates that HMS-REST and HEC-HMS can be effectively integrated and demonstrates that the use of HEC-HMS with a web-based interface can facilitate pragmatic applications of the HEC-HMS model in the fields of model calibration, flood simulation and forecasting, and participatory decision-making.

Evaluating soil erosion and runoff dynamics in a humid subtropic, low stream order, southern plains watershed from cultivation and solar farm development

Much work has been done to understand and improve soil and water conservation where agriculture has driven land use intensification. Less is known about soil- and water-related impacts from intensification driven by solar farming, especially at watershed-scales. Here we employed Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to model Pond Creek, a rural watershed in Texas, USA. Land use is primarily crop cultivation and secondarily pasture for cattle grazing. Presently, several industrial-scale projects are planned to convert ≈15–30% of Pond Creek from agriculture to solar farms. The model was parameterized using public data sources and information from local stakeholders, then calibrated to several historical precipitation events. Experiments were conducted by varying precipitation depth, duration, and land uses: native vegetation pre-cultivation (control), cultivation (current), current conditions with 15% solar farm conversion (solar), and current conditions with 30% solar farm conversion (solar x2). Shifting to solar farming led to significant increases in cumulative sediment load (+12%–30%), with no significant differences in peak discharge rate changes (+0.38%–4%). Comparison to soil loss tolerance values showed current and solar treatment erosion rates exceeded tolerance values between 0.17 and 2.29 tons per hectare and all treatments were significantly different than the native treatment. We discuss high leverage strategies applicable to solar farm development sites as well as watersheds where they reside. Accelerating demand for land for renewable energy such as solar farming warrants greater attention from the soil and water conservation community to anticipate and mitigate impacts across landscapes.

Development of flood inundation maps for the Chaliyar Basin, Kerala under climate change scenarios

Abstract Floods are one of the extreme events and widespread natural disasters that significantly affect the civil infrastructure and livelihoods of people. Recently, climate change has significantly altered the rainfall pattern and increased flood events worldwide, especially in India. Therefore, it has become essential to map potential flood inundation regions for various future extreme events to develop appropriate flood mitigation and management strategies. This study aims to develop flood inundation maps for different return periods under climate change scenarios for the Chaliyar basin, Kerala. The Hydrologic Engineering Center-Hydrologic Modelling System model was used to simulate streamflow under SSP2-4.5 and SSP5-8.5 scenarios. Later, flood inundation maps were developed for different return periods using the Hydrologic Engineering Center-River Analysis System model. It was observed that for the near future (2031–2040) and far future (2071–2080), simulated streamflow is higher for SSP5-8.5. However, the mid-future (2051–2060) resulted in a higher streamflow for SSP2-4.5 than the SSP5-8.5 scenario. A maximum of 19.52 m of water surface elevation occurred at Kizhupparamba during mid-future for SSP2-4.5, followed by 18.38 m of water surface elevation at Cheekode during the near future for SSP5-8.5, for 100-year return period events. This study showed that hydrologic and hydraulic models could be effectively combined for mapping the flood inundation areas.

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.

Application of hydrological models in climate change framework for a river basin in India

Abstract Soil Water Assessment Tool (SWAT), Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and Hydrologic Simulation Program Fortran (HSPF) are explored for streamflow simulation of Lower Godavari Basin, India. The simulating ability of models is evaluated using four indicators. SWAT has shown exceptional simulating ability in calibration and validation compared to the other two. Accordingly, SWAT is used in the climate change framework using an ensemble of 13 Global Climate Models and four Shared Socioeconomic Pathways (SSPs). Three time segments, near-future (2021–2046), mid-future (2047–2072), and far-future (2073–2099), are considered for analysis. Four SSPs show a substantial increase in streamflow compared to the historical period (1982–2020). These deviations range from 17.14 (in SSP245) to 28.35% (in SSP126) (near-future), 31.32 (SSP370) to 43.28% (SSP585) (mid-future), and 30.41 (SSP126) to 70.8% (SSP585) (far-future). Across all timescales covering 948 months, the highest projected streamflows observed in SSP126, SSP245, SSP370, and SSP585 were 4962.36, 6,108, 6,821, and 6,845 m3/s, respectively. Efforts are also made to appraise the influence of multi-model combinations on streamflow. The present study is expected to provide a platform for holistic decision-making, which helps develop efficient basin planning and management alternatives.

Modeling tropical storm Elsa: Flood map simulation using multisensory precipitation in Connecticut

A flood map simulation in the Fenton River watershed, Connecticut, was conducted for Tropical Storm Elsa occurred in early July 2021, using Multi Radar Multi Sensor-Quantitative Precipitation Estimation (MRMS-QPE) as input to force the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) to simulate discharges in the mainstream of the watershed. The simulated discharges were calibrated using observed discharges at the Old Turnpike Bridge USGS station, and they were used to force a Hydrologic Engineering Center-River Analysis System (HEC-RAS) 2D model of the Fenton River watershed. The simulated stages were calibrated using observed stages at Old Turnpike Bridge USGS station to simulate flood maps in the mainstream of the watershed. The resulting use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE precipitation shows that these models set up is user-friendly. The model shows stability and the capacity to simulate flood maps along the whole mainstream of the Fenton River with good accuracy.

Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling

ABSTRACT This study evaluates the performance of the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil and Water Assessment Tool (SWAT), feedforward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and multilinear regression (MLR) for modelling the rainfall–runoff–sediment process in Katar catchment, Ethiopia. Afterward, neural network ensemble (NE), weighted average ensemble (WE) and simple average ensemble (SE) techniques were developed to improve the performance of single models. The performance of the models was evaluated using Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ANFIS model outperformed the other single models for rainfall–runoff–sediment modelling. Moreover, the integration of artificial intelligence and physically-based models resulted in improved performance, with the NE technique demonstrating better accuracy by improving individual models by 5.8–27.6% for rainfall–runoff and 3.59–37.9% for suspended sediment load modelling in the validation phase.

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.