Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

Förster, Kristian; Hanzer, Florian; Stoll, Elena; Scaife, Adam A.; MacLachlan, Craig; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich

doi:10.5194/hess-22-1157-2018

Cited by 8 publications

(10 citation statements)

References 74 publications

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“…Previously, both dynamical and statistical models have been used to predict, for example, precipitation over the British Isles (Baker et al 2018a;Ossó et al 2018), precipitation in Europe (Dunstone et al 2018;Totz et al 2017), the wintertime NAO index (Dobrynin et al 2018;Hall et al 2017;Scaife et al 2014;Stockdale et al 2015), snow accumulation over the Alps (Förster et al 2018), sea ice cover in the Baltic Sea (Karpechko et al 2015), wintertime European temperatures (Folland et al 2012), and monthly temperatures at various midlatitude locations (Karpechko 2015).…”

Section: Several Potential Sources Of Predictability Have Beenmentioning

confidence: 99%

Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe

et al. 2019

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A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons.

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Section: Several Potential Sources Of Predictability Have Beenmentioning

confidence: 99%

Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe

et al. 2019

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“…The ESP method was originally developed in the snow-dominated catchments of the western United States (e.g., Franz et al, 2003), but has shown skill in other regions, including the UK (Harrigan et al, 2018), European Alps (Förster et al, 2018), Sweden (Girons Lopez et al, 2020), New Zealand (Singh, 2016), Australia (Pagano et al, 2010;Wang et al, 2011), and China (Yuan et al, 2016). Simplicity and efficiency make ESP a popular choice for operational forecasting.…”

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confidence: 99%

Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

Donegan¹,

Murphy²,

Harrigan³

et al. 2020

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Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores of 0.8 (1-day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Results also highlight the potential utility of ESP for decision-making, as measured by its ability to forecast low and high flow events. In addition to our benchmarking experiment, we conditioned ESP on the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill of 7–18 % were possible over lead times of 1 to 3 months, and that NAO-conditioned ESP is particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

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“…The choice of the appropriate complexity depends on the objective of the work. Förster et al (2018) aims at forecasting February SWE anomalies spatially-averaged at the catchment scale, so they employed a simple hydrological snow model driven by air temperature and precipitation anomalies only, at coarse (monthly) time resolution. An advantage of this simple approach is the limited input data requirement and the low computational load of the simulations, at the expense of higher uncertainty in the output results.…”

Section: Impact Of the Choice Of The Snow Modelmentioning

confidence: 99%

“…This approach allows to reduce the uncertainty associated the reference data compared to more common cases in which reference data are simulated by hydrological models and model errors affect the quality of the reference data (i.e. Förster et al, 2018).…”

Section: Uncertainty In the Validation Datamentioning

confidence: 99%

“…These studies highlight the importance of a reliable representation of mountain snowpack for improving streamflow forecasts in mountain areas. An original approach to seasonal hydrological forecasting in mountain areas is to change the focus from the prediction of instantaneous hydrological fluxes (rainfall, streamflow) to that of slowly varying, and probably more predictable, hydrological quantities, such as the snow water equivalent (Förster et al, 2018). Snowpack is a natural "integrator" of the climatic conditions over multiple days/months, so even if daily temperature and precipitation forecasts do not match the corresponding observations, the differences may compensate over monthly/seasonal time scales and provide reasonable monthly/seasonal snowpack forecasts.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal forecasting of snow resources at Alpine sites

Terzago

Bongiovanni

Hardenberg

2022

Preprint

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Abstract. Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple time scales, and seasonal forecasts can help in the optimization of the available snow/water resources with lead times of several months. In the framework of the ERA4CS MEDSCOPE project we developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with starting date November 1st and lead times of 7 months, so up to May 31st of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and of mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts, visualization of the forecast outputs. In this paper we present the modeling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps, and finally used as input for the physically-based multi-layer snow model SNOWPACK. The RainFARM stochastic downscaling procedure is applied to precipitation data in order to allow an estimate of uncertainties due to the downscaling method. The skill of the prototype in predicting the monthly snow depth evolution from November to May in each season of the hindcast period 1995–2015 are demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows very good skills at predicting the tercile category, i.e. snow depth below- and above-normal, in the winter (lead time 2-3-4 months) and spring (lead times 5-6-7 months) ahead: snow depth is predicted with higher accuracy (Brier Skill Score) and higher discrimination (Area Under the ROC Curve Skill Score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead time 1 and 2 months (November and December) but also at lead time 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. No remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled or bias-corrected and downscaled compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

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Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

Cited by 8 publications

References 74 publications

Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe

Statistical Learning Methods as a Basis for Skillful Seasonal Temperature Forecasts in Europe

Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

Seasonal forecasting of snow resources at Alpine sites

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