19 research outputs found
Analysis of changes in climate and river discharge with focus on seasonal runoff predictability in the Aksu River Basin
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Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century
During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can
have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science
Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to
better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed
with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and
models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and
potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global
change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts
Impact of climate change on the streamflow in the glacierized Chu River Basin, Central Asia
A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia
The study presents a statistically based seasonal
precipitation forecast model, which automatically identifies suitable
predictors from globally gridded sea surface temperature (SST) and climate variables by means of an extensive data-mining procedure and explicitly avoids the utilization of typical large-scale climate indices. This leads to an enhanced flexibility of the model and enables its automatic calibration for any target area without any prior assumption concerning adequate predictor variables.
Potential predictor variables are derived by means of a cell-wise
correlation analysis of precipitation anomalies with gridded global climate
variables under consideration of varying lead times. Significantly
correlated grid cells are subsequently aggregated to predictor regions by
means of a variability-based cluster analysis. Finally, for every month and
lead time, an individual random-forest-based forecast model is constructed,
by means of the preliminary generated predictor variables. Monthly
predictions are aggregated to running 3-month periods in order to
generate a seasonal precipitation forecast.
The model is applied and evaluated for selected target regions in central
and south Asia. Particularly for winter and spring in westerly-dominated
central Asia, correlation coefficients between forecasted and observed
precipitation reach values up to 0.48, although the variability of
precipitation rates is strongly underestimated. Likewise, for the monsoonal
precipitation amounts in the south Asian target area, correlations of up to
0.5 were detected. The skill of the model for the dry winter season over
south Asia is found to be low.
A sensitivity analysis with well-known climate indices, such as the El Niño–
Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East
Atlantic (EA) pattern, reveals the major large-scale controlling mechanisms of the
seasonal precipitation climate for each target area. For the central Asian
target areas, both ENSO and NAO are identified as important controlling
factors for precipitation totals during moist winter and spring seasons.
Drought conditions are found to be triggered by a cold ENSO phase in
combination with a positive state of NAO in northern central Asia, and by
cold ENSO conditions in combination with a negative NAO phase in southern
central Asia. For the monsoonal summer precipitation amounts over southern
Asia, the model suggests a distinct negative response to El Niño events
Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management
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 2Â 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