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| Titel |
Hydrological recurrence as a measure for large river basin classification and process understanding |
| VerfasserIn |
R. Fernandez, T. Sayama |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-22), S.1919-1942 |
| Datensatznummer |
250120691
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| Publikation (Nr.) |
 copernicus.org/hess-19-1919-2015.pdf |
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| Zusammenfassung |
Hydrological functions of river basins are summarized as collection, storage
and discharge, which can be characterized by the dynamics of hydrological
variables including precipitation, evaporation, storage and runoff. The
temporal patterns of each variable can be indicators of the functionality of
a basin. In this paper we introduce a measure to quantify the degree of
similarity in intra-annual variations at monthly scale at different years for
the four main variables. We introduce this measure under the term of
recurrence and define it as the degree to which a monthly hydrological
variable returns to the same state in subsequent years. The degree of
recurrence in runoff is important not only for the management of water
resources but also for the understanding of hydrologic processes, especially
in terms of how the other three variables determine the recurrence in runoff.
The main objective of this paper is to propose a simple hydrologic
classification framework applicable to large basins at global scale based on
the combinations of recurrence in the four variables using a monthly scale
time series. We evaluate it with lagged autocorrelation (AC), fast Fourier
transforms (FFT) and Colwell's indices of variables obtained from the
EU-WATCH data set, which is composed of eight global hydrologic model
(GHM) and land surface model (LSM) outputs. By setting a threshold to define high
or low recurrence in the four variables, we classify each river basin into 16
possible classes.
The overview of recurrence patterns at global scale suggested that
precipitation is recurrent mainly in the humid tropics, Asian monsoon area
and part of higher latitudes with an oceanic influence. Recurrence in
evaporation was mainly dependent on the seasonality of energy availability,
typically high in the tropics, temperate and sub-arctic regions. Recurrence
in storage at higher latitudes depends on energy/water balances and snow,
while that in runoff is mostly affected by the different combinations of
these three variables. According to the river basin classification, 10 out of
the 16 possible classes were present in the 35 largest river basins in the
world. In the humid tropic region, the basins belong to a class with high
recurrence in all the variables, while in the subtropical region many of the
river basins have low recurrence. In the temperate region, the energy limited or
water limited in summer characterizes the recurrence in storage, but runoff
exhibits generally low recurrence due to the low recurrence in
precipitation. In the sub-arctic and arctic regions, the amount of snow also
influences the classes; more snow yields higher recurrence in storage and
runoff. Our proposed framework follows a simple methodology that can aid in
grouping river basins with similar characteristics of water, energy and
storage cycles. The framework is applicable at different scales with
different data sets to provide useful insights into the understanding of
hydrologic regimes based on the classification. |
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