HEC-HMS based rainfall-runoff model for Punpun river basin

Ranjan, Shashi; Singh, V. P.

doi:10.2166/wpt.2022.033

Cited by 26 publications

(3 citation statements)

References 37 publications

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“…Runoff generation directly relates to the curve number value [44]. Consequently, the smaller value of CN denotes a low runoff coefficient, whereas the higher number denotes a high runoff coefficient [45]. The slope of the basin can also have a big impact on how much runoff occurs [46].…”

Section: Figure 5 Soil Classification Map Of Irawan Watershedmentioning

confidence: 99%

Extreme Event-based Rainfall-runoff Simulation Utilizing GIS Techniques in Irawan Watershed, Palawan, Philippines

2023

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River flow assessments and ecologically sustainable water management plans are now possible due to the advancement of sophisticated computer models. The US Army Corps of Engineers developed the HEC-HMS model, which can be used for various hydrological simulations. Rainfall-runoff modeling aids in estimating peak flows, which is critical for water resource management planning. On December 18, 2017, a heavy rainfall event in the ungauged Irawan basin in Puerto Princesa City, Palawan, Philippines, was simulated to determine the peak flow and amount of water. The current research aims to construct a rainfall-runoff simulation model. A specific hyetograph is used to make the hydrographs for the basin. This study utilizes ArcGIS and QGIS, which perform the geospatial analysis and provide the HEC-HMS model's hydrologic modeling inputs. The hydrological parameters were determined using soil type, land use, and land cover maps. Incorporating SCS loss, Clark unit hydrograph, and Muskingum flow routing, HEC-HMS was employed in the rainfall-runoff simulation. Rainfall data corresponding to the recorded streamflow was used to calibrate and validate the parameters. Several performance metrics, including Nash-Sutcliffe efficiency (NSE) and Percentage Bias (PBIAS), were utilized to evaluate the overall effectiveness of the system. An effective decision-making and warning system can be implemented using the developed model. Doi: 10.28991/CEJ-2023-09-01-017 Full Text: PDF

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Section: Figure 5 Soil Classification Map Of Irawan Watershedmentioning

confidence: 99%

Extreme Event-based Rainfall-runoff Simulation Utilizing GIS Techniques in Irawan Watershed, Palawan, Philippines

2023

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“…It is also called the coefficient of efficiency. It provides the proportions of variance of the observation for a model and is widely used in hydrology [19]. NSE has a range between -∞ to 1, and a value of 1 indicates a perfect match.…”

Section: ) Nash-sutcliffe Efficiency (Nse) Hydrological Indicementioning

confidence: 99%

ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin

Ranjan

Singh

2023

Eng. Technol. Appl. Sci. Res.

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The Punpun River is primarily a rain-fed river. Forecasting rainfall accurately would enable an early evaluation of drought and flooding conditions. Therefore, having a flawless model for predicting rainfall is important for the hydrological analysis of any river basin. In this study, Artificial Neural Network (ANN)-based models were developed to predict rainfall and discharge in the basin. During the rainy season, water is spread in and around the area of the watershed, thus a General Regression Neural Network (GRNN)-based model was proposed for fast estimation of the inundation area during the flood taking as input cross-section, rainfall, and discharge. The proposed ANN-GRNN coupled model is the first of its kind for this study area. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the water-spreading area.

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“…Kaffas and Hrissanthou (2014) used a continuous rainfallrunoff model to study a watershed in the Kosynthos River in Greece, and the results were highly satisfactory when compared to actual field discharge measurements. Vivekanand Singh and Shishir Ranjan (2022), demonstrated that monsoonal rainfall runoff models provided better results for the Punpun basin. It was found that the monthly model outperformed the daily and monsoonal models.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variations of Surface runoff of Saroor Nagar Urban Watershed of Telangana Using Geospatial and Artificial Intelligence Techniques

Vaddiraju,

Talari

2024

Preprint

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Hydrological modelling is an effective tool for predicting the hydrological response of a watershed to develop strategies for efficient water management. The overarching goal of this study was to simulate rainfall-runoff processes in the Saroor Nagar urban watershed, Telangana, for the years 2008, 2014, 2020, and 2026 using geospatial and deep learning techniques. The Land Use Land Cover (LULC) maps of 2008, 2014, and 2020 are generated using the Support Vector Machine(SVM) algorithm, and a Cellular-Automata-based artificial neural network model is used to simulate the LULC for the year 2026. Daily rainfall in 2026 is forecasted using the Long- Short Term Memory (LSTM) technique using univariate daily rainfall data from twenty-two years from 2000–2022. The HEC-HMS model is used to simulate the rainfall-runoff process. The infiltration loss, the conversion of the excess rainfall into surface runoff, and the flow routing of the channel reach were all modelled using the SCS-Curve Number method, the SCS unit hydrograph method, and the Muskingum routing method. As the watershed is ungauged, the calibration and validation of the proposed model are performed by considering the discharge data of the Hussain Sagar catchment using the method of regionalization. The performance of the model is evaluated using the coefficient of determination (R2) and Nash-Sutcliffe Efficiency (NSE). The study revealed that the built-up area increased by 55.18% at the cost of vegetation and barren land during 2008–2020, and the waterbodies have by 29% during 2008–2020 due to the removal of encroachments and measures taken by the Government of Telangana to protect lakes. The vegetation layer has increased by a marginal 6%, whereas barren land has decreased drastically by 75.8%. The future LULC of 2026, simulated using the CA-ANN technique, revealed that the built-up area is expected to increase further by 17.4% at the cost of other LULC classes. The waterbodies are expected to decrease by 8.3%, the vegetation by 17.7%, and the barren land is expected to reduce to 1.25%. The annual rainfall in 2026 is expected to be 1484mm. The analysis of the HEC-HMS model reveals that the simulated peak discharge increased from 44.4 m3/s to 57.1 m3/s during 2008–2020 and is expected to increase to 66.1 m3/s in the year 2026. During calibration, R2 was 0.88 and NSE was 0.75, and during validation, the R2 values were 0.83 and 0.89, respectively. The study's findings clearly demonstrate the proposed model's ability to adequately simulate stream flow in the basin and can be taken as an input for judicious use of water resources in the watershed.

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HEC-HMS based rainfall-runoff model for Punpun river basin

Cited by 26 publications

References 37 publications

Extreme Event-based Rainfall-runoff Simulation Utilizing GIS Techniques in Irawan Watershed, Palawan, Philippines

Extreme Event-based Rainfall-runoff Simulation Utilizing GIS Techniques in Irawan Watershed, Palawan, Philippines

ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin

Spatio-Temporal Variations of Surface runoff of Saroor Nagar Urban Watershed of Telangana Using Geospatial and Artificial Intelligence Techniques

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