Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

Saloranta, Tuomo

doi:10.5194/tc-6-1323-2012

Cited by 85 publications

(87 citation statements)

References 12 publications

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“…The seNorge snow model (Saloranta, 2012(Saloranta, , 2014(Saloranta, , 2016) is a temperature-index model which requires only data of air temperature and precipitation. In addition, the seNorge snow model includes a compaction module that can be used to assimilate and validate snow depth rather than snow cover only.…”

Section: Modified Senorge Modelmentioning

confidence: 99%

“…The aim of this study is to estimate SWE and snowmelt runoff in a Himalayan catchment by assimilating remotely sensed snow cover and in situ snow depth observations into a modified version of the seNorge snow model (Saloranta, 2012(Saloranta, , 2014(Saloranta, , 2016. Climate sensitivity tests are subsequently performed to investigate the change of SWE and snowmelt runoff as result of changing air temperature and precipitation.…”

Section: Introductionmentioning

confidence: 99%

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Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

et al. 2017

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Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

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Section: Modified Senorge Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

et al. 2017

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“…In the south-western region, however, DDD_LN performs better than DDD_G, which underestimates SCA. This region is characterized by a very rugged topography, which may influence both the estimates of SCA derived from the MODIS satellite and the quality of the meteorological grid, as very few meteorological observations are located at high altitudes (Dyrrdal et al, 2012;Saloranta, 2012). Further investigations relating the errors in estimating SCA to other CCs, such as catchment gradients, may explain the difference in results between DDD_LN and DDD_G.…”

Section: Discussionmentioning

confidence: 97%

“…This meteorological grid is denoted V1. Recently, a new, improved meteorological grid was developed, denoted V2 (Lussana et al 2014a, b), which reduced much of the positive bias in precipitation characteristic of V1 (see Saloranta, 2012). The new meteorological grid (V2) in DDD gives reasonable simulated values of runoff without the need for a calibrated correction of the amount of precipitation (θ Pc ; see Table 1 for parameters of the DDD model).…”

Section: Test Of Sd_g In Dddmentioning

confidence: 99%

A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

Skaugen

Weltzien

2016

The Cryosphere

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Abstract. Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G), whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN). The two models are implemented in the parameter parsimonious rainfall-runoff model Distance Distribution Dynamics (DDD), and their capability for predicting runoff, SWE and snow-covered area (SCA) is tested and compared for 71 Norwegian catchments. The calibration period is 1985-2000 and validation period is 2000-2014. Results show that SD_G better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash-Sutcliffe and Kling-Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

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“…Daily updated maps of snow conditions have been produced for Norway since 2004 by using the seNorge snow model (www.seNorge.no; Tveito et al, 2002;Engeset et al, 2004;Saloranta, 2012Saloranta, , 2014aSaloranta, , b, 2016 and the seNorge conventional climatological datasets as model forcing data. The simulated snow maps are used among others by the avalanche and flood forecasting services, hydropower energy situation analysis, as well as the general public.…”

Section: Impact On the Senorge Snow Model Simulationsmentioning

confidence: 99%

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

Lussana

Saloranta

Skaugen

et al. 2018

Earth Syst. Sci. Data

106

127

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Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successivecorrection schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957-2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at

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Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

Cited by 85 publications

References 12 publications

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

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