Department Structural Change, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Department Structural Change, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Iulii Didovets
Research Department II: Climate Resilience, Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A 31, 14473 Potsdam, Germany
Department Structural Change, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
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Total article views: 3,355 (including HTML, PDF, and XML)
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3,161
140
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3,355
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72
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PDF: 140
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Supplement: 60
BibTeX: 61
EndNote: 72
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630
252
131
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38
55
HTML: 630
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Total: 1,013
BibTeX: 38
EndNote: 55
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Total article views: 4,368 (including HTML, PDF, and XML)
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Total article views: 3,355 (including HTML, PDF, and XML)
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Total article views: 1,013 (including HTML, PDF, and XML)
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This social relevant work is innovative, well-thought-out, and technically sound. The study has the potential to support water management in an under-served region that would benefit from this kind of operational forecast system (Central Asia). Free public availability of the author’s code and all the required input data means the forecast model is easily implemented in production systems.
This social relevant work is innovative, well-thought-out, and technically sound. The study has...
Seasonal streamflow forecasts for snowmelt-dominated catchments often rely on snowpack data, which are not always available and are prone to errors. Our study evaluates near-real-time global snow estimates and climate oscillation indices for predictions in the data-scarce mountains of central Asia. We show that climate indices can improve prediction accuracy at longer lead times, help offset snow data uncertainty, and enhance predictions where streamflow depends on in-season climate variability.
Seasonal streamflow forecasts for snowmelt-dominated catchments often rely on snowpack data,...