Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management
Authors: Apel Heiko , Abdykerimova Zharkinay , Agalhanova Marina , Baimaganbetov Azamat , Gavrilenko Nadejda , Gerlitz Lars , Kalashnikova Olga , Unger-Shayesteh Katy , Vorogushyn Sergiy , Gafurov Abror 
Published: April 2018

Abstract. The semi-arid regions of Central Asia crucially depend on the water resources
supplied by the mountainous areas of the Tien Shan and Pamir and Altai
mountains. During the summer months the snow-melt- and glacier-melt-dominated river
discharge originating in the mountains provides the main water resource
available for agricultural production, but also for storage in reservoirs for
energy generation during the winter months. Thus a reliable seasonal forecast
of the water resources is crucial for sustainable management and planning
of water resources. In fact, seasonal forecasts are mandatory tasks of all
national hydro-meteorological services in the region. In order to support the
operational seasonal forecast procedures of hydro-meteorological services,
this study aims to develop a generic tool for deriving statistical
forecast models of seasonal river discharge based solely on observational
records. The generic model structure is kept as simple as possible in order
to be driven by meteorological and hydrological data readily available at the
hydro-meteorological services, and to be applicable for all catchments in the
region. As snow melt dominates summer runoff, the main meteorological
predictors for the forecast models are monthly values of winter precipitation
and temperature, satellite-based snow cover data, and antecedent discharge.
This basic predictor set was further extended by multi-monthly means of the
individual predictors, as well as composites of the predictors. Forecast
models are derived based on these predictors as linear combinations of up to
four predictors. A user-selectable number of the best models is extracted
automatically by the developed model fitting algorithm, which includes a test
for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction
model. Forecasts of the mean seasonal discharge of the period April to
September are derived every month from January until June. The
application of the model for several catchments in Central Asia – ranging
from small to the largest rivers (240 to 290 000 km2 catchment
area) – for the period 2000–2015 provided skilful forecasts for most
catchments already in January, with adjusted R2 values of the best model
in the range of 0.6–0.8 for most of the catchments. The skill of the
prediction increased every following month, i.e. with reduced lead time, with
adjusted R2 values usually in the range 0.8–0.9 for the best and 0.7–0.8
on average for the set of models in April just before the prediction
period. The later forecasts in May and June improve further due to the high
predictive power of the discharge in the first 2 months of the snow melt
period. The improved skill of the set of forecast models with decreasing lead
time resulted in narrow predictive uncertainty bands at the beginning of the
snow melt period. In summary, the proposed generic automatic forecast model
development tool provides robust predictions for seasonal water availability
in Central Asia, which will be tested against the official forecasts in the
upcoming years, with the vision of operational implementation.