Abstract
Abstract. 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).
Highlights
The steady increase in computational capabilities together with the expanding accessibility of regional and global datasets trigger the development of regional- to continental-scale and global-scale hydrological models (Archfield et al, 2015), hereafter referred to as macro-scale models.During the last decade, several of these macro-scale models have become operational and continuously provide data automatically for decision-making
The application of the geostatistical method total negative deviation topkriging (TNDTK) through an leave-one-out cross-validation (LOOCV) procedure reveals an agreement between empirical values and predictions as shown in Figs. 5 and 6
This figure clearly shows that for 9 out of 11 target sites the geostatistical method TNDTK outperforms E-HYdrological Predictions for the Environment (HYPE) in prewww.hydrol-earth-syst-sci.net/22/4633/2018/
Summary
The steady increase in computational capabilities together with the expanding accessibility of regional and global datasets (e.g. soil properties, land-cover, morphology, climate characteristics, satellite-based gridded precipitation) trigger the development of regional- to continental-scale and global-scale hydrological models (Archfield et al, 2015), hereafter referred to as macro-scale models. Several of these macro-scale models have become operational and continuously provide data automatically for decision-making. Other macro-scale models are used for off-line water assessments and research purposes. A. Pugliese et al.: Geostatistical data assimilation for macro-scale hydrological models to many South American basins; and the SWIM model (Krysanova et al, 1998) couples water balance simulations with water quality for small to mid-size watersheds, i.e. regional meso-scale
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