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A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations
A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations
Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).
- Alma Mater Studiorum University of Bologna Italy
- TU Wien Austria
- Swedish Meteorological and Hydrological Institute Sweden
- TU Wien Austria
- TU Wien Austria
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Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., and Siebert, S.: Development and testing of the WaterGAP 2 global model of water use and availability, Hydrolog. Sci. J., 48, 317-337, https://doi.org/10.1623/hysj.48.3.317.45290, 2003. [OpenAIRE]
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Figure 11. LNSE ratio (see Eq. 12) as a function of stream gauge availability: black dots represent the average of 11 LNSE ratio values, while crosses indicate their 95 % confidence interval. eEnvironment, IFIP Advances in Information and Communication Technology, Springer, Berlin, Heidelberg, 657-666, https://doi.org/10.1007/978-3-642-22285-6_71, 2011.
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Blöschl, G., Sivapalan, M., Thorsten, W., Viglione, A., and Savenije, H.: Runoff prediction in ungauged basins: synthesis across processes, places and scales, Cambridge University Press, Cambridge, 2013.
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Citations provided by BIP!Popularity provided by BIP!- Alma Mater Studiorum University of Bologna Italy
- TU Wien Austria
- Swedish Meteorological and Hydrological Institute Sweden
- TU Wien Austria
- TU Wien Austria
- TU Wien Austria
- TU Wien Austria
- Swedish Meteorological and Hydrological Institute, Rossby Centre Sweden
- GECOSISTEMA SRL Italy
- GECOSISTEMA SRL Italy
Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).