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Title: Forecasting reservoir inflows using remotely sensed precipitation estimates: a pilot study for the River Naryn, Kyrgyzstan
Authors: Dixon, Samuel G.
Wilby, Robert L.
Keywords: Remotely sensed precipitation
River flow forecast
Toktogul reservoir
Regression model
Issue Date: 2015
Publisher: © IAHS. Published by Taylor & Francis
Citation: DIXON, S.G. and WILBY, R.L., 2015. Forecasting reservoir inflows using remotely sensed precipitation estimates: a pilot study for the River Naryn, Kyrgyzstan. Hydrological Sciences Journal, 61(1), pp. 107-122.
Abstract: This study explores the feasibility of applying remotely sensed precipitation estimates (in this case from the Tropical Rainfall Measuring Mission [TRMM]) for forecasting inflows to the strategically important Toktogul reservoir in the Naryn basin, Kyrgyzstan. Correlations between observed precipitation at Naryn and 0.5° TRMM totals is weaker for daily (r = 0.25) than monthly (r = 0.93) totals, but the Naryn gauge is representative of monthly TRMM precipitation estimates across ~60% of the basin. We evaluate predictability of monthly inflows given TRMM estimates, air temperature and antecedent flows. Regression model skill was superior to the zero order forecast (mean flow) for lead-times up to three months, and had lower errors in estimated peaks. Over 80% of the variance in monthly inflows is explained with three-month lead, and up to 65% for summer half-year average. The analysis also reveals zones that are delivering highest predictability and hence candidate areas for surface network expansion.
Description: This is an Accepted Manuscript of an article published by Taylor & Francis in Hydrological Sciences Journal on 11/11/2015, available online: http://wwww.tandfonline.com/10.1080/02626667.2015.1006227.
Version: Accepted for publication
DOI: 10.1080/02626667.2015.1006227
URI: https://dspace.lboro.ac.uk/2134/20127
Publisher Link: http://dx.doi.org/10.1080/02626667.2015.1006227
ISSN: 0262-6667
Appears in Collections:Published Articles (Geography and Environment)

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