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Simulating water resource availability under data scarcity : a case study for the Ferghana Valley (Central Asia)

Radchenko, Iuliia ; Breuer, Lutz ; Forkutsa, Irina ; Frede, Hans-Georg

Originalveröffentlichung: (2014) Water 6(11):3270-3299 doi:10.3390/w6113270
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URN: urn:nbn:de:hebis:26-opus-115213

Freie Schlagwörter (Englisch): hydrological modeling , Monte Carlo simulation , MODAWEC , HBV-light model , lapse rate
Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-UniversitĂ€t Gießen
Institut: Institut fĂŒr Landschaftsökologie und Ressourcenmanagement
Fachgebiet: Agrarwissenschaften und Umweltmanagement
DDC-Sachgruppe: Landwirtschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2014
Publikationsdatum: 29.06.2015
Kurzfassung auf Englisch: Glaciers and snowmelt supply the Naryn and Karadarya rivers, and about 70% of the water available for the irrigated agriculture in the Ferghana Valley. Nineteen smaller catchments contribute the remaining water mainly from annual precipitation. The latter will gain importance if glaciers retreat as predicted. Hydrological models can visualize such climate change impacts on water resources. However, poor data availability often hampers simulating the contributions of smaller catchments. We tested several data pre-processing methods (gap filling, MODAWEC (MOnthly to DAily WEather Converter), lapse rate) and their effect on the performance of the HBV (Hydrologiska ByrÄns Vattenavdelning)-light model. Monte Carlo simulations were used to define parameter uncertainties and ensembles of behavioral model runs. Model performances were evaluated by constrained measures of goodness-of-fit criteria (cumulative bias, coefficient of determination, model efficiency coefficients (NSE) for high flow and log-transformed flow). The developed data pre-processing arrangement can utilize data of relatively poor quality (only monthly means or daily data with gaps) but still provide model results with NSE between 0.50 and 0.88. Some of these may not be accurate enough to directly guide water management applications. However, the pre-processing supports producing key information that may initiate rigging of monitoring facilities, and enable water management to respond to fundamentally changing water availability.
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