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The first aim of the present paper is the determination of the magnitude, annual variation and spatial distribution of skill in seasonal hindcasts of runoff and discharge in the entire continent of South America. We evaluated 35 years of hindcasts generated with the Variable Infiltration Capacity (VIC) hydrological model forced with SEAS5 hindcasts. Initial conditions of terrestrial water and so-called pseudo-observations were computed with a reference (i.e. historic) simulation. Skill was determined with monthly temporal resolution for the entire annual cycle and mostly using the pseudo-observations for verification. The second aim of the paper is the explanation of skill in terms of its sources, namely meteorological forcing and the initial conditions. Therefore, two sets of restricted hindcasts, which isolate the sources of skill, were analysed. The SEAS5 precipitation hindcasts exhibit significant skill even at the longest lead times (7 months). Beyond the first lead month we found significant skill in 13–43 % of the grid cells, depending on target and lead month. Levels of skill are higher in the full hindcasts (significant skill in 31–89 % of the grid cells). At the continental scale more of the skill is caused by the forcing than by the initial conditions. The runoff hindcasts are skilful in large parts of the continent. In a 1000 km wide band along the north coast of the continent and in southeast South America, most of the skill is due to the forcing. A typical feature of these regions is an increase of skill with lead time during specific parts of the year, which is against the common tendency. In Argentina and north Chile most of the skill in the runoff hindcasts can be attributed to the initial conditions of soil moisture. Verification with real observations of discharge broadly confirmed the skill pattern obtained with pseudo-observations.

Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations’ observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations’ observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

This paper uses hindcasts (1981-2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mai

Seasonal predictions of river flow can be exploited among others to optimise hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. This paper presents the development of the system and the evaluation of its skill. The variable infiltration capacity (VIC) hydrological model is forced with bias-corrected output of ECMWF's seasonal forecast system 4. For the assessment of skill, we analysed hindcasts (1981-2010) against a reference run, in which VIC was forced by gridded meteorological observations. The reference run was also used to generate initial hydrological conditions for the hindcasts. The skill in run-off and discharge hindcasts is analysed with monthly temporal resolution, up to 7 months of lead time, for the entire annual cycle. Using the reference run output as pseudo-observations and taking the correlation coefficient as metric, hot spots of significant theoretical skill in discharge and run-off were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, northern Germany, Romania and Bulgaria (mainly from November to January), western France (from December to May) and the eastern side of Great Britain (January to April). Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. In some areas some skill persists even at the longest lead times (7 months). Theoretical skill was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is generally substantially less than theoretical skill. This effect is stronger for small basins than for large basins. Qualitatively, the use of different skill metrics (correlation coefficient; relative operating characteristics, ROC, area; an

Impacts of climate change at 1.5, 2 and 3 °C mean global warming above preindustrial level are investigated and compared for runoff, discharge and snowpack in Europe. Ensembles of climate projections representing each of the warming levels were assembled to describe the hydro-meteorological climate at 1.5, 2 and 3 °C. These ensembles were then used to force an ensemble of five hydrological models and changes to hydrological indicators were calculated. It is seen that there are clear changes in local impacts on evapotranspiration, mean, low and high runoff and snow water equivalent between a 1.5, 2 and 3 °C degree warmer world. In a warmer world, the hydrological impacts of climate change are more intense and spatially more extensive. Robust increases in runoff affect the Scandinavian mountains at 1.5 °C, but at 3 °C extend over most of Norway, Sweden and northern Poland. At 3 °C, Norway is affected by robust changes in all indicators. Decreases in mean annual runoff are seen only in Portugal at 1.5 °C warming, but at 3 °C warming, decreases to runoff are seen around the entire Iberian coast, the Balkan Coast and parts of the French coast. In affected parts of Europe, there is a distinct increase in the changes to mean, low and high runoff at 2 °C compared to 1.5 °C, strengthening the case for mitigation to lower levels of global warming. Between 2 and 3 °C, the changes in low and high runoff levels continue to increase, but the changes to mean runoff are less clear. Changes to discharge in Europe’s larger rivers are less distinct due to the lack of homogenous and robust changes across larger river catchments, with the exception of Scandinavia where discharges increase with warming level.

Increasing temperatures and snow scarce winter seasons challenge the winter tourism industry. In this study the impacts of +2 °C global warming on winter tourism demand in Europe's ski tourism related NUTS-3 regions are quantified. Using time series regression models, the relationship between natural snow conditions and monthly overnight stays is estimated. Based on these model results, we quantify the risk of tourism demand losses due to weather variability and assess the potential impacts of climate change. Hereby, the concept of Weather-Value at Risk (0.95) is applied. Snow data are provided by the hydrological model VIC, which is forced by E-OBS data to obtain historical snow values for tourism model calibration and forced by EURO-CORDEX climate simulations to obtain snow projections until 2100. Under +2 °C warming, the weather-induced risk of losses in winter overnight stays related to skiing tourism in Europe amounts to up to 10.1 million nights per winter season, which is +7.3 million overnight stays additionally at risk compared to the reference period (1971–2000). Among the top four European skiing tourism nations – Austria, France, Italy and Switzerland – France and Switzerland show the lowest increase in risk of losses in winter overnight stays. The highest weather-induced risk of losses in winter overnight stays – in the reference period as well as in the +2 °C scenarios – is found in Austria, followed by Italy. These two countries account for the largest fraction of winter overnight stays in skiing related NUTS-3 regions.