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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 4
Hydrol. Earth Syst. Sci., 19, 1919–1942, 2015
https://doi.org/10.5194/hess-19-1919-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 1919–1942, 2015
https://doi.org/10.5194/hess-19-1919-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Apr 2015

Research article | 22 Apr 2015

Hydrological recurrence as a measure for large river basin classification and process understanding

R. Fernandez1,2 and T. Sayama1,3 R. Fernandez and T. Sayama
  • 1International Centre for Water Hazard and Risk Management, Public Works Research Institute, Tsukuba, Ibaraki, Japan
  • 2National Graduate Institute for Policy Studies, Tokyo, Japan
  • 3Now at Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan

Abstract. 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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