Snowmelt runoff modelling in an arid mountain watershed, Tarim Basin, China

Li, Xingong; Williams, Mark W.

doi:10.1002/hyp.7098

Cited by 104 publications

(68 citation statements)

References 27 publications

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“…A popular approach is to derive snow depletion curves from MODIS SCF and use them in the Snowmelt Runoff Model (SRM) (Martinec, 1975). This approach is still popular and used in recent studies (Lee et al, 2005;Tekeli et al, 2005;Li and Williams, 2008;Butt and Bilal, 2011;Tahir et al, 2011;Bavera et al, 2012). However, the SRM studies are focussed mostly on the winter half-year and are limited to study sites where snowmelt processes are dominant.…”

Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction -SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by cooccurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

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Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Generally, regions with a large contribution of sensible heat flux to the heat balance tend to have low degree-day factors (Hock, 2003). DDF S are expected to increase with increasing elevation and increasing snow density (Li and Williams, 2008). Forest regions often have lower values of DDF S than open regions (Rango and Martinec, 1995).…”

Section: Z H He Et Al: Estimating Degree-day Factors From Modis Fomentioning

confidence: 99%

Estimating degree-day factors from MODIS for snowmelt runoff modeling

Parajka

Tian

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDF S ) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDF S values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDF S estimated from snow data with those using spatially uniform DDF S calibrated on runoff. The runoff performances using estimated DDF S are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km 2 . Approximately 70 % of the basin is covered by snow in the early spring season.

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“…In improved HBV (IHBV), overland flow (the dashed line in Figure 1) is calculated by the Green-Ampt infiltration equation (Equation (1)) [46] and snowmelt is estimated using a modified degree-day equation as shown by Equation (2). In this Equation, degree-day factor (Cf max ) is a key parameter that varies by several variables such as air temperature, elevation, snow albedo, solar radiation and so on [47,48]. Thus, the term e gamaˆpT-T t q is added to simulate the effect of air temperature fluctuations on the degree-day factor.…”

Section: Improved Hbv Model Descriptionmentioning

confidence: 99%

Towards an Efficient Rainfall–Runoff Model through Partitioning Scheme

Dariane

Javadianzadeh

2016

Water

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Partitioning Scheme (PS) is one of the strategies that could play a constructive role in improving the performance of conceptual rainfall-runoff (CRR) models. The main objective of this paper is to develop a Rainfall Runoff-Partitioning Scheme (RR-PS) with the ability of distinguishing different flow regimes in a basin and simulating each regime separately. The model utilizes a combination of PS and "regular" procedures and is equipped with Fuzzy C-Means (FCM) and Seasonal Partitioning (SP) to recognize different flow regimes. In addition, to avoid excessive increase of the model parameters caused by PS, sensitivity analysis is used to recognize the sensitive parameters. The PS part of integrated model is only run for the "sensitive" parameters and the "regular" part of model is implemented for the "less-sensitive" parameters. Data from three different scale basins in USA and Iran are used to evaluate the models. A HBV-Light (Hydrologiska Byråns Vattenbalansavdelning-Light model) based CRR model (Improved HBV-IHBV) is developed in FORTRAN (Formula Translation) with several modifications for testing the procedures. The results show that in all cases IHBV-based models using PS method perform better than the regular IHBV model. Overall, IHBV-SP demonstrates better performance than the other PS based models. Further improvement is reached by adopting user-defined seasons in IHBV-SP through optimization.

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Snowmelt runoff modelling in an arid mountain watershed, Tarim Basin, China

Cited by 104 publications

References 27 publications

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Estimating degree-day factors from MODIS for snowmelt runoff modeling

Towards an Efficient Rainfall–Runoff Model through Partitioning Scheme

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