Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model

Bhuiyan, Hassan A. K. M.; McNairn, Heather; Powers, Jarrett; Merzouki, A.

doi:10.3390/hydrology4010009

Cited by 51 publications

(26 citation statements)

References 38 publications

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“…Despite the larger computational demand, implementation of TVSA can be useful in model calibration by identifying event-based hydrological processes. This is particularly important for event-based calibration useful for flood forecasting in cold regions dominated by snowmelt runoff [67,75]. Furthermore, the choice of error metric has significant influence on the rankings and selection of parameter sensitivity.…”

Section: Discussionmentioning

confidence: 99%

Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Bajracharya

Awoye

Stadnyk

et al. 2020

Water

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The complex terrain, seasonality, and cold region hydrology of the Nelson Churchill River Basin (NCRB) presents a formidable challenge for hydrological modeling, which complicates the calibration of model parameters. Seasonality leads to different hydrological processes dominating at different times of the year, which translates to time variant sensitivity in model parameters. In this study, Hydrological Predictions for the Environment model (HYPE) is set up in the NCRB to analyze the time variant sensitivity analysis (TVSA) of model parameters using a Global Sensitivity Analysis technique known as Variogram Analysis of Response Surfaces (VARS). TVSA can identify parameters that are highly influential in a short period but relatively uninfluential over the whole simulation period. TVSA is generally effective in identifying model’s sensitivity to event-based parameters related to cold region processes such as snowmelt and frozen soil. This can guide event-based calibration, useful for operational flood forecasting. In contrast to residual based metrics, flow signatures, specifically the slope of the mid-segment of the flow duration curve, allows VARS to detect the influential parameters throughout the timescale of analysis. The results are beneficial for the calibration process in complex and multi-dimensional models by targeting the informative parameters, which are associated with the cold region hydrological processes.

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Section: Discussionmentioning

confidence: 99%

Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Bajracharya

Awoye

Stadnyk

et al. 2020

Water

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“…Many diverse criteria are used to assess the performance efficiency of hydrological models [61]. Based on the literature [27,62,63] the following were used to compare the performance of the flow model in relation to the observed flows: NSE-broadly used for calibration and validation of hydrological models regarding discharge, r-primarily used for evaluation of the timing of simulated and observed time series, PBias-used to investigate the tendency of over-or underestimation of simulated flow, and rPFD-important criterion in terms of flood risk.…”

Section: Calibration and Validationmentioning

confidence: 99%

Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Gilewski

Nawalany

2018

Water

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Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.

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“…Model performance was evaluated based on the most common statistical model comparison tools and other additional parameters, such as model user-friendliness in operational forecasting and required computing time. There are several statistical model performance evaluation criteria employed for model optimization and for comparison of the accuracy of different models (Gupta et al 1998, Hall 2001, Krause et al 2005, MacLean 2005, Moriasi et al 2007, Golmohammadi et al 2014, Amirhossien et al 2015, Bhuiyan et al 2017. In this study, the Nash-Sutcliffe efficiency criterion (NSE; Nash and Sutcliffe 1970), the correlation coefficient (r), root mean squared error (RMSE), mean absolute relative error (MARE) and deviation of runoff volume (D v ) were selected.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed

Unduche

Tolossa

Senbeta

et al. 2018

Hydrological Sciences Journal

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The complexities of the Prairie watersheds, including potholes, drainage interconnectivities, changing land-use patterns, dynamic watershed boundaries and hydro-meteorological factors, have made hydrological modelling on Prairie watersheds one of the most complex task for hydrologists and operational hydrological forecasters. In this study, four hydrological models (WATFLOOD, HBV-EC, HSPF and HEC-HMS) were developed, calibrated and tested for their efficiency and accuracy to be used as operational flood forecasting tools. The Upper Assiniboine River, which flows into the Shellmouth Reservoir, Canada, was selected for the analysis. The performance of the models was evaluated by the standard statistical methods: the Nash-Sutcliffe efficiency coefficient, correlation coefficient, root mean squared error, mean absolute relative error and deviation of runoff volumes. The models were evaluated on their accuracy in simulating the observed runoff for calibration and verification periods (2005-2015 and 1994-2004, respectively) and also their use in operational forecasting of the 2016 and 2017 runoff.

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Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model

Cited by 51 publications

References 38 publications

Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed

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