Snowmelt - Runoff Model for Stream Flow Forecasts

Martinec, J.

doi:10.2166/nh.1975.0010

Cited by 235 publications

(154 citation statements)

References 1 publication

(2 reference statements)

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“…This result is coherent with the findings presented by Clark et al (2006). Using a model which incorporates snow cover area as a state variable, such as the snowmelt runoff model (SRM; Martinec, 1974) or the Soil and Water Assessment Tool (SWAT; Arnold et al, 1998), could overcome the issue of non-linearities between variables, while using recursive or smoother approaches to data assimilation could help with the time lag issue between observations and state variables.…”

Section: Sca Data Assimilationsupporting

confidence: 87%

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Bergeron

Trudel

Leconte

2016

Hydrol. Earth Syst. Sci.

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Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

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Section: Sca Data Assimilationsupporting

confidence: 87%

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Bergeron

Trudel

Leconte

2016

Hydrol. Earth Syst. Sci.

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“…(3) where,QZ is the discharge (m 3 /s) from zone Z, Qr and Qs are the discharge (m 3 /s) from direct precipitation and snow and ice melt, respectively, C is runoff coefficient with Cr referring to rain and Cs to snow and ice melt as mentioned in Martinec (1975) and Qb is the base flow (m 3 /s). The discharge Q is then routed to the basin outlet as per the recession equation 4 given by Martinec (1975).…”

Section: M= {Ddfsnow/ice/debris X T If Tmentioning

confidence: 99%

Comparative Assessment of Runoff and Its Components in Two Catchments of Upper Indus Basin by Using a Semi Distributed Glacio-Hydrological Model

Ali

Bano

Kayastha

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The hydrology of Upper Indus basin is not recognized well due to the intricacies in the climate and geography, and the scarcity of data above 5000 m a.s.l where most of the precipitation falls in the form of snow. The main objective of this study is to measure the contributions of different components of runoff in Upper Indus basin. To achieve this goal, the Modified positive degree day model (MPDDM) was used to simulate the runoff and investigate its components in two catchments of Upper Indus basin, Hunza and Gilgit River basins. These two catchments were selected because of their different glacier coverage, contrasting area distribution at high altitudes and significant impact on the Upper Indus River flow. The components of runoff like snow-ice melt and rainfall-base flow were identified by the model. The simulation results show that the MPDDM shows a good agreement between observed and modeled runoff of these two catchments and the effects of snow and ice are mainly reliant on the catchment characteristics and the glaciated area. For Gilgit River basin, the largest contributor to runoff is rain-base flow, whereas large contribution of snow-ice melt observed in Hunza River basin due to its large fraction of glaciated area. This research will not only contribute to the better understanding of the impacts of climate change on the hydrological response in the Upper Indus, but will also provide guidance for the development of hydropower potential and water resources assessment in these catchments.

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“…Several studies show different strategies with respect to how hydrological models can benefit from MODIS snow cover data. 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).…”

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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Snowmelt - Runoff Model for Stream Flow Forecasts

Cited by 235 publications

References 1 publication

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Comparative Assessment of Runoff and Its Components in Two Catchments of Upper Indus Basin by Using a Semi Distributed Glacio-Hydrological Model

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

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