A steady-state saturation model to determine the subsurface travel time (STT) in complex hillslopes

Abstract:A one-dimensional uncoupled model governed by this research is a physics-based modelling of the rainfall-runoff induced erosion process. The presented model is composed of three parts of a three-dimensional (3D) hillslope geometry, a nonlinear storage (kinematic wave) model for hillslope hydrological response, and an unsteady physically based surface erosion model. The 3D hillslope geometry model allows describing of the hillslope morphology by defining their plan shape and profile curvature. By changing these two topographic parameters, nine basic hillslope types are derived. The modelling of hillslope hydrological response is based on a flow continuity equation as the relation of discharge and flow depth is passed on kinematic wave approximation. The erosion model is based on a mass conservation equation for unsteady flow. The model assumes that suspended sediment does not affect flow dynamics. The model also accounts for the effect of flow depth plus loose soil depth on soil detachment. The presented model was run for two different precipitations, slope content, and length, and results were plotted for sediment detachment/deposition rate. Based on the obtained results, in hillslopes with convex and straight profile curvatures, sediment detachment only occurred in the whole length of the hillslope. However, in concave ones, sediment detachment and deposition only occurred together in hillslope. The hillslopes with straight profiles and convergent plans have the highest rate of detachment. Also, results show that most detachment rates occur in convex profile curvatures, which are about 15 times more than in straight profiles.

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“…The normalized plan form geometry and profile shape function is given respectively (Agnese et al ., ; Sabzevari1 et al ., ):

_{p l} ((), ξ) = \frac{A ((), x)}{w ((), x)} = \frac{true {true\int}_{0}^{L ξ} c_{w} e x p ([], c_{s} {(1 - ξ)}^{(2 - n)}) d ξ}{c_{w} e x p ([], c_{s} {(1 - ξ)}^{(2 - n)})}

_{p r} ((), ξ) = \frac{S ((), x)}{\overset{true¯}{s}} = \frac{(||, \frac{d z}{d x}) = n \frac{H}{L} {(1 - \frac{x}{L})}^{n - 1} = n \overset{s}{true} {(1 - ξ)}^{n - 1}}{\overset{s}{true} = H true/ L} = n {(1 - ξ)}^{n - 1}

…”

Section: Introductionmentioning

confidence: 99%

Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes

et al. 2016

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“…Based on their results, the divergent hillslopes can exhibit travel times that are double those of the convergent hillslopes, and the concave slopes tend to have lower travel times than planar or convex slopes. Sabzevari et al (2010) and Sabzevari (2010) studied the rate of saturation of complex hillslopes based on the subsurface flow. They have also investigated the effects of saturation upon the travel times of complex hillslopes and proved that the rates of saturation in complex hillslopes are not the same.…”

Section: Introductionmentioning

confidence: 99%

Time of concentration of surface flow in complex hillslopes

Sabzevari¹,

Saghafian²,

Talebi³

et al. 2013

Journal of Hydrology and Hydromechanics

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Abstract. Time of concentration (TC) of surface flow in watersheds depends on the coupled response of hillslopes and stream networks. The important point in this background is to study the effects of the geometry and the shape of complex hillslopes on the time of concentration considering the degree of flow convergence (convergent, parallel or divergent) as well as the profile curvature (concave, straight or convex). In this research, the shape factor of complex hillslopes as introduced by Agnese et al. (2007) is generalized and linked to the TC. A new model for calculating TC of complex hillslopes is presented, which depends on the plan shape, the type and degree of profile curvature, the Manning roughness coefficient, the flow regime, the length, the average slope, and the excess rainfall intensity. The presented model was compared to that proposed by Singh and Agiralioglu (1981a,b) and Agiralioglu (1985). Moreover, the results of laboratory experiments on the travel time of surface flow of complex hillslopes were used to calibrate the model. The results showed that TC for convergent hillslopes is nearly double those of parallel and divergent ones. TC in convex hillslopes was very close to that in straight and concave hillslopes. While the effect of convergence on TC is considerable, the curvature effect confirmed insignificant. Finally, in convergent hillslopes, TC increases with the degree of convergence, but in divergent hillslopes, it decreases as degree of divergence increases.

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“…The travel time parameter for subsurface and groundwater flows was calculated from basin characteristics using a method based on effective porosity, soil hydraulic conductivity, routing length, and average bedrock slope (Henderson and Wooding, 1964;Sabzevari et al, 2010). Both effective porosity and soil hydraulic conductivity were determined according to soil texture.…”

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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A steady-state saturation model to determine the subsurface travel time (STT) in complex hillslopes

Cited by 28 publications

References 46 publications

Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes

Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes

Time of concentration of surface flow in complex hillslopes

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

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