OPTIMAL IDENTIFICATION OF MUSKINGUM ROUTING COEFFICIENTS<sup>1</sup>

Wu, Jy S.; King, Ellis L.; Wang, Michael

doi:10.1111/j.1752-1688.1985.tb00151.x

Cited by 11 publications

(7 citation statements)

References 3 publications

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“…The flow travel time was calculated from the routing length and average flow velocity. The dimensionless weighting factor controls the level of attenuation, ranging from 0 (maximum attenuation) to 0.5 (no attenuation), and can be determined by a number of techniques (Wu et al, 1985;Kshirsagar et al, 1995). However, information for approximating this parameter is lacking, so a medium value of 0.25 was assigned for the basin.…”

Section: Routing Parametersmentioning

confidence: 99%

Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

Fang

Pomeroy

Ellis

et al. 2013

Hydrol. Earth Syst. Sci.

114

151

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Abstract.One of the purposes of the Cold Regions Hydrological Modelling platform (CRHM) is to diagnose inadequacies in the understanding of the hydrological cycle and its simulation. A physically based hydrological model including a full suite of snow and cold regions hydrology processes as well as warm season, hillslope and groundwater hydrology was developed in CRHM for application in the Marmot Creek Research Basin (∼ 9.4 km 2 ), located in the Front Ranges of the Canadian Rocky Mountains. Parameters were selected from digital elevation model, forest, soil, and geological maps, and from the results of many cold regions hydrology studies in the region and elsewhere. Non-calibrated simulations were conducted for six hydrological years during the period 2005-2011 and were compared with detailed field observations of several hydrological cycle components. The results showed good model performance for snow accumulation and snowmelt compared to the field observations for four seasons during the period 2007-2011, with a small bias and normalised root mean square difference (NRMSD) ranging from 40 to 42 % for the subalpine conifer forests and from 31 to 67 % for the alpine tundra and treeline larch forest environments. Overestimation or underestimation of the peak SWE ranged from 1.6 to 29 %. Simulations matched well with the observed unfrozen moisture fluctuation in the top soil layer at a lodgepole pine site during the period 2006-2011, with a NRMSD ranging from 17 to 39 %, but with consistent overestimation of 7 to 34 %. Evaluations of seasonal streamflow during the period 2006-2011 revealed that the model generally predicted well compared to observations at the basin scale, with a NRMSD of 60 % and small model bias (1 %), while at the sub-basin scale NRMSDs were larger, ranging from 72 to 76 %, though overestimation or underestimation for the cumulative seasonal discharge was within 29 %. Timing of discharge was better predicted at the Marmot Creek basin outlet, having a Nash-Sutcliffe efficiency (NSE) of 0.58 compared to the outlets of the sub-basins where NSE ranged from 0.2 to 0.28. The Pearson product-moment correlation coefficient of 0.15 and 0.17 for comparisons between the simulated groundwater storage and observed groundwater level fluctuation at two wells indicate weak but positive correlations. The model results are encouraging for uncalibrated prediction and indicate research priorities to improve simulations of snow accumulation at treeline, groundwater dynamics, and small-scale runoff generation processes in this environment. The study shows that improved hydrological cycle model prediction can be derived from improved hydrological understanding and therefore is a model that can be applied for prediction in ungauged basins.

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Section: Routing Parametersmentioning

confidence: 99%

Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

Fang

Pomeroy

Ellis

et al. 2013

Hydrol. Earth Syst. Sci.

114

151

View full text Add to dashboard Cite

show abstract

“…From the average flow velocity and routing length, the storage constant was estimated. The dimensionless weighting factor controls the level of attenuation, ranging from 0 (maximum attenuation) to 0.5 (no attenuation), and can be approximated by a variety of methods (Wu et al, 1985;Kshirsagar et al, 1995). However, due to lack of information for the approximation, medium value, 0.25 was assumed for the basin.…”

Section: Routing Parametersmentioning

confidence: 99%

Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

Fang

Pomeroy

Westbrook

et al. 2010

Hydrol. Earth Syst. Sci.

123

114

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Abstract. The Cold Regions Hydrological Modelling platform (CRHM) was used to create a prairie hydrological model for Smith Creek Research Basin (∼400 km 2 ), eastcentral Saskatchewan, Canada. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were defined and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland. Model parameters were estimated using field survey data, LiDAR digital elevation model (DEM), SPOT 5 satellite imageries, stream network and wetland inventory GIS data. Model simulations were conducted for 2007/2008 and 2008/2009. No calibration was performed. The model performance in predicting snowpack, soil moisture and streamflow was evaluated against field observations. Root mean square differences (RMSD) between simulation and observations ranged from 1.7 to 25.2 mm and from 4.3 to 22.4 mm for the simulated snow accumulation in 2007/2008 and 2008/2009, respectively, with higher RMSD in grassland, river channel, and open water HRUs. Spring volumetric soil moisture was reasonably predicted compared to a point observation in a grassland area, with RMSD of 0.011 and 0.009 for 2008 and 2009 simulations, respectively. The model was able to capture the timing and magnitude of peak spring basin discharge, but it underestimated the cumulative volume of basin discharge by 32% and 56% in spring 2008 and 2009, respectively. The results suggest prediction of Canadian Prairie basin snow hydrology is possible with no calibration if physically based models are used with physically meaningful model parameters that are derived from high resolution geospatial data.

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“…This data set has also been extensively studied by others (Gill, 1978;Tung, 1984;Yoon and Padmanabhan, 1993;and Mohan, 1997). The third case is based on the data reported by Wu et al (1985). It was shown by Wang and Sing (1992) that the outflow hydrograph for this case could be best represented by Muskingum parameters varying along the river reach.…”

Section: Case Studiesmentioning

confidence: 95%

“…Observed and computed values of outflow for data set given byWu et al (1985) characteristics. The computed values of outflow in…”

mentioning

confidence: 99%

Approximate Methods for the Estimation of Muskingum Flood Routing Parameters

Alhumoud

Esen

2006

Water Resour Manage

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There are a variety of techniques for estimating the parameters x and K of the Muskingum method of flood routing. One common difficulty in all the approaches is that different storm sequences along the same river reach would typically yield different parameter estimates. The a statistical analysis of these parameters also shows that they are highly variable. As a result achieving of a high level accuracy may not be the principle issue in describing x and K. This paper presents two approximate methods for estimating these parameters rather easily. The first method requires the computation of the slopes of the inflow and outflow hydrographs at their point of intersection, and the computation of the maximum storage within the reach. The second method requires the computation of the inflow and outflow hydrographs at two specific points. Three case studies investigated show that the first method gives estimates for the Muskingum parameters comparable to those derived by traditional estimation procedures for hydrographs showing linear characteristics.

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OPTIMAL IDENTIFICATION OF MUSKINGUM ROUTING COEFFICIENTS¹

Cited by 11 publications

References 3 publications

Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

Approximate Methods for the Estimation of Muskingum Flood Routing Parameters

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OPTIMAL IDENTIFICATION OF MUSKINGUM ROUTING COEFFICIENTS1

Cited by 11 publications

References 3 publications

Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

Approximate Methods for the Estimation of Muskingum Flood Routing Parameters

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OPTIMAL IDENTIFICATION OF MUSKINGUM ROUTING COEFFICIENTS¹