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B. Decharme
,
H. Douville
,
A. Boone
,
F. Habets
, and
J. Noilhan

. This work, like that of Sivapalan et al. (1987) , accounts for saturation excess runoff generation (Dunne process) via TOPMODEL, but also includes a separate infiltration excess mechanism (Horton runoff) using a Philip approximation to the infiltration capacity. Based on these studies, Stieglitz et al. (1997) performed a comparison between a single-column model where k sat was taken to be vertically homogeneous, and another single-column model where k sat declined with depth and was much

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Philippe Crochet
,
Tómas Jóhannesson
,
Trausti Jónsson
,
Oddur Sigurðsson
,
Helgi Björnsson
,
Finnur Pálsson
, and
Idar Barstad

at a fine horizontal resolution (1 km) and various time scales (day, month, season, year, climatology). The model is driven by 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data of temperature, wind, and precipitation to produce 6-hourly precipitation estimates. The free parameters of the model are adjusted by comparison with precipitation observations at low elevation from a rain gauge network and with precipitation derived from mass balance measurements

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Mekonnen Gebremichael
,
Thomas M. Over
, and
Witold F. Krajewski

resolution at nadir); and a nonattenuating wavelength (∼10 cm instead of 2.2 cm). Comparison of the scaling characteristics of rainfall derived from the PR and GR is therefore instrumental to identifying the potential advantages and limitations of the PR. In light of the above, the overall objective of this paper is to investigate, compare, and contrast the scaling characteristics of rainfall derived from the PR and GR in terms of the scaling parameter estimates; the relationship between the scaling

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T. J. Bellerby

-based precipitation estimation technique Fig . 4. A map of the study area showing the location and range of the Melbourne radar Fig . 5. An example 15-min 0.15° feature-based satellite precipitation product compared to coincident ground-radar data Fig . 6. Scatterplot of the 15-min 0.15° feature-based satellite precipitation product against coincident ground-radar data Fig . 7. A comparison of frequency histograms for 15-min 0.15° precipitation products from the feature-based, optimised GPI, and histogram

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Michael Hobbins
,
Andrew Wood
,
David Streubel
, and
Kevin Werner

compared our climatological warm-season [i.e., May–October (MJJASO)] E pan surface to the widely used version of the same in the Farnsworth et al. (1982) atlas (map 1; as this latter map is not available in a useful digital format, this comparison is not shown here). However, various caveats apply to the comparison. First, the Farnsworth et al. (1982) map is not simply interpolated climatological mean warm-season E pan : the source data are drawn from various pan types—class-A pans, sunken pans

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Ardeshir M. Ebtehaj
,
Rafael L. Bras
, and
Efi Foufoula-Georgiou

retrieval algorithm. Rather, we explore the idea of “implicitly” encoding the land surface information content across all available frequency channels, as part of the recently proposed Bayesian methodology, called Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) by Ebtehaj et al. (2015) , briefly reviewed in section 2 . In essence, the a priori database in this Bayesian algorithm is properly organized in an algebraically tractable manner via two fat matrices, called

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Luis Gimeno
,
Raquel Nieto
,
Ricardo M. Trigo
,
Sergio M. Vicente-Serrano
, and
Juan Ignacio López-Moreno

the IP in that vertical column gained moisture; such regions were therefore designated as moisture source regions. In contrast, air masses in transit to the IP over regions where precipitation dominated evaporation ( E − P < 0) had a net loss of moisture; these were designated moisture sink regions. Thus, the ( E − P ) 1 map identifies those regions in which the air masses gained ( E − P > 0) or lost ( E − P < 0) moisture in the day before they arrived in the IP. This temporal

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Thomas Marke
,
Ulrich Strasser
,
Florian Hanzer
,
Johann Stötter
,
Renate Anna Irma Wilcke
, and
Andreas Gobiet

downscaling methods that allow for application and comparison to observations at shorter (i.e., from daily to hourly) time scales, particularly in hydrological studies. For mountain (natural) snow cover modeling, a lot of scientific effort has been put into the discussion of which type of model to use for what purpose ( Klemes 1990 ; WMO 1986 ). Simple and computationally inexpensive temperature-index models have proven to be suitable for snowmelt modeling, sometimes even outperforming the more complex

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Valentijn R. N. Pauwels
and
Gabriëlle J. M. De Lannoy

assimilate six consecutive runoff observations (this could be any number; in this example the number six was randomly chosen), the system state starting 10 time steps before the first observation, up to the state at the time of the last observation, thus in total 16 time steps, has to be updated. The size of the assimilation window (the number of observations taken into account) can be chosen based on computational efficiency, or an optimal value can be determined based on a comparison between the

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Konstantine P. Georgakakos

example to follow preserves the seasonal climate division precipitation maps in each trial. These Monte Carlo trials are used to produce significance thresholds in estimator values by determining the values of the estimators among all those obtained during the Monte Carlo trials that delineate a preset lower quantile (e.g., decile for a 10% significance level). The values of the estimators obtained from the application to the real data (not the Monte Carlo runs) that are lower than the significance

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