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. The semi-arid regions of Central Asia crucially depend on the water resourcessupplied by the mountainous areas of the Tien Shan and Pamir and Altaimountains. During the summer months the snow-melt- and glacier-melt-dominated riverdischarge originating in the mountains provides the main water resourceavailable for agricultural production, but also for storage in reservoirs forenergy generation during the winter months. Thus a reliable seasonal forecastof the water resources is crucial for sustainable management and planningof water resources. In fact, seasonal forecasts are mandatory tasks of allnational hydro-meteorological services in the region. In order to support theoperational seasonal forecast procedures of hydro-meteorological services,this study aims to develop a generic tool for deriving statisticalforecast models of seasonal river discharge based solely on observationalrecords. The generic model structure is kept as simple as possible in orderto be driven by meteorological and hydrological data readily available at thehydro-meteorological services, and to be applicable for all catchments in theregion. As snow melt dominates summer runoff, the main meteorologicalpredictors for the forecast models are monthly values of winter precipitationand temperature, satellite-based snow cover data, and antecedent discharge.This basic predictor set was further extended by multi-monthly means of theindividual predictors, as well as composites of the predictors. Forecastmodels are derived based on these predictors as linear combinations of up tofour predictors. A user-selectable number of the best models is extractedautomatically by the developed model fitting algorithm, which includes a testfor robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every predictionmodel. Forecasts of the mean seasonal discharge of the period April toSeptember are derived every month from January until June. Theapplication of the model for several catchments in Central Asia – rangingfrom small to the largest rivers (240 to 290 000 km 2 catchmentarea) – for the period 2000–2015 provided skilful forecasts for mostcatchments already in January, with adjusted R2  values of the best modelin the range of 0.6–0.8 for most of the catchments. The skill of theprediction increased every following month, i.e. with reduced lead time, withadjusted R2  values usually in the range 0.8–0.9 for the best and 0.7–0.8on average for the set of models in April just before the predictionperiod. The later forecasts in May and June improve further due to the highpredictive power of the discharge in the first 2 months of the snow meltperiod. The improved skill of the set of forecast models with decreasing leadtime resulted in narrow predictive uncertainty bands at the beginning of thesnow melt period. In summary, the proposed generic automatic forecast modeldevelopment tool provides robust predictions for seasonal water availabilityin Central Asia, which will be tested against the official forecasts in theupcoming years, with the vision of operational implementation.

hydrology and earth system sciences

Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management