Considering floods as multivariate events allows a better statistical representation of their complexity. In this work the relevance of multivariate analysis of floods for designing or assessing the safety of hydraulic structures is discussed. A flood event is characterized by its peak flow and volume. The dependence between the variables is modelled with a copula. One thousand random pairs of variables are transformed to hydrographs, applying the Beta distribution function. Synthetic floods are routed through a reservoir to assess the risk of overtopping a dam. The resulting maximum water levels are compared to estimations considering the peak flow and volume separately. The analysis is performed using daily flows observed in the River Agrio in Neuquén Province, Argentina, a catchment area of 7300 km 2 . The bivariate approach results in higher maximum water level values. Therefore the multivariate approach should be preferred for the estimation of design variables.
Fluvial sediment dynamics plays a key role in the Amazonian environment, with most of the sediments originating in the Andes. The Madeira River, the second largest tributary of the Amazon River, contributes up to 50% of its sediment discharge to the Atlantic Ocean, most of it provided by the Andean part of the Madeira basin, in particular the Beni River. In this study, we assessed the rainfall (R)-surface suspended sediment concentration (SSSC) and discharge (Q)-SSSC relationship at the Rurrenabaque station (200 m a.s.l.) in the Beni Andean piedmont (Bolivia). We started by showing how the R and Q relationship varies throughout the hydrological year (September to August), describing a counter-clockwise hysteresis, and went on to evaluate the R–SSSC and Q–SSSC relationships. Although no marked hysteresis is observed in the first case, a clockwise hysteresis is described in the second. In spite of this, the rating curve normally used ( SSSC = aQ b ) shows a satisfactory R2 = 0.73 (p < 0.05). With regard to water discharge components, a linear function relates the direct surface flow Qs–SSSC, and a hysteresis is observed in the relationship between the base flow Qb and SSSC. A higher base flow index (Qb/Q) is related to lower SSSC and vice versa. This article highlights the role of base flow on sediment dynamics and provides a method to analyze it through a seasonal empirical model combining the influence of both Qb and Qs, which could be employed in other watersheds. A probabilistic method to examine the SSSC relationship with R and Q is also proposed.
Long and continuous series of precipitation in a high temporal resolution are required for several purposes, namely, urban hydrological applications, design of flash flood control structures, etc. As data of the temporally required resolution is often available for short period, it is advantageous to develop a precipitation model to allow for the generation of long synthetic series. A stochastic model is applied for this purpose, involving an alternating renewal process (ARP) describing a system consisting of spells that can take two possible states: wet or dry. Stochastic generation of rainfall time series using ARP models is straight forward for single site simulation. The aim of this work is to present an extension of the model to spatio-temporal simulations. The proposed methodology combines an occurrence model to define in which locations rainfall events occur simultaneously with a multivariate copula to generate synthetic events. Rainfall series registered in different regions of Germany are used to develop and test the methodology. Results are compared with an existing method in which long independent time series of rainfall events are transformed to spatially dependent ones by permutation of their order. The proposed model shows to perform as a satisfactory extension of the ARP model for multiple sites simulations.
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