A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation

A multivariate analysis on flood variables is needed to design some hydraulic structures like dams, as the complexity of the routing process in a reservoir requires a representation of the full hydrograph. In this work, a bivariate copula model was used to obtain the bivariate joint distribution of flood peak and volume, in order to know the probability of occurrence of a given inflow hydrograph. However, the risk of dam overtopping is given by the maximum water elevation reached during the routing process, which depends on the hydrograph variables, the reservoir volume and the spillway crest length. Consequently, an additional bivariate return period, the so-called routed return period, was defined in terms of risk of dam overtopping based on this maximum water elevation obtained after routing the inflow hydrographs. The theoretical return periods, which give the probability of occurrence of a hydrograph prior to accounting for the reservoir routing, were compared with the routed return period, as in both cases hydrographs with the same probability will draw a curve in the peak-volume space. The procedure was applied to the case study of the Santillana reservoir in Spain. Different reservoir volumes and spillway lengths were considered to investigate the influence of the dam and reservoir characteristics on the results. The methodology improves the estimation of the Design Flood Hydrograph and can be applied to assess the risk of dam overtoppin

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

The performance of distributed hydrological models depends on the resolution, both spatial and temporal, of the rainfall surface data introduced. The estimation of quantitative precipitation from meteorological radar or satellite can improve hydrological model results, thanks to an indirect estimation at higher spatial and temporal resolution. In this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km2 spatial resolution, provided by the Catalan Meteorological Service, is used to feed the RIBS distributed hydrological model. A Window Probability Matching Method (gage-adjustment method) is applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation in both convective and stratiform Z/R relations used over Catalonia. Once the rainfall field has been adequately obtained, an advection correction, based on cross-correlation between two consecutive images, was introduced to get several time resolutions from 1 min to 30 min. Each different resolution is treated as an independent event, resulting in a probable range of input rainfall data. This ensemble of rainfall data is used, together with other sources of uncertainty, such as the initial basin state or the accuracy of discharge measurements, to calibrate the RIBS model using probabilistic methodology. A sensitivity analysis of time resolutions was implemented by comparing the various results with real values from stream-flow measurement stations

Evaluación del hormigón de los espigones del balneario Monte Hermoso (Prov. de Buenos Aires)

En el presente trabajo se estudiaron las causas del deterioro del hormigón de dos espigones de la ciudad balnearia de Monte Hermoso (Prov. de Bs. As.) construidos a principios de la década del ´70. El avanzado estado de deterioro sufrido por los pilotes, hizo que al tiempo se repararan mediante un encamisado de hormigón simple. El proceso de corrosión de las armaduras siguió generando tensiones en el hormigón y al poco tiempo, el propio encamisado se agrietó y desprendió. Los cabezales de pilotes y vigas superiores de ambos espigones sufrieron un proceso corrosivo tal que, debieron ser demolidos. Para caracterizar el hormigón, se realizaron ensayos de densidad, absorción y porosidad y se determinó el contenido de cloruros totales y libres y de sulfatos solubles. Complementariamente, se realizó un estudio petrográfico del hormigón y de los agregados. Se analizó el contacto hormigón-armadura y se evaluaron los productos de oxidación utilizando DRX. Los estudios y ensayos realizados confirman que el deterioro de los pilotes de los espigones de Monte Hermoso, ubicados en ambiente marino, se debió a la corrosión de la armadura por presencia de cloruros

Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia

AbstractAn overwhelming number of applications depend on reliable precipitation estimations. However, over complex terrain in regions such as the Andes or the southwestern Amazon, the spatial coverage of rain gauges is scarce. Two reanalysis datasets, a satellite algorithm and a scheme that combines surface observations with satellite estimations were selected for studying rainfall in the following areas of Bolivia: the central Andes, Altiplano, southwestern Amazonia, and Chaco. These Bolivian regions can be divided into three main basins: the Altiplano, La Plata, and Amazon. The selected reanalyses were the Modern-Era Retrospective Analysis for Research and Applications, which has a horizontal resolution (~50km) conducive for studying rainfall in relatively small precipitation systems, and the Climate Forecast System Reanalysis and Reforecast, which features an improved horizontal resolution (~38km). The third dataset was the seventh version of the Tropical Rainfall Measurement Mission 3B42 algorithm, which is conducive for studying rainfall at an ~25km horizontal resolution. The fourth dataset utilizes a new technique known as the Combined Scheme, which successfully removes satellite bias. All four of these datasets were aggregated to a coarser resolution. Additionally, the daily totals were calculated to match the cumulative daily values of the ground observations. This research aimed to describe and compare precipitations in the two reanalysis datasets, the satellite-algorithm dataset, and the Combined Scheme with ground observations. Two seasons were selected for studying the precipitation estimates: the rainy season (December–February) and the dry season (June–August). The average, bias, standard deviation, correlation coefficient, and root mean square error were calculated. Moreover, a contingency table was generated to calculate the accuracy, bias frequency, probability of detection, false alarm ratio, and equitable threat score.All four datasets correctly depicted the spatial rainfall pattern. However, CFSR and MERRA overestimated precipitation along the Andes' eastern-facing slopes and exhibited a dry bias over the eastern Amazon; TRMM3B42 and the Combined Scheme depicted a more realistic rainfall distribution over both the Amazon and the Andes. When separating the precipitation into classes, MERRA and CFSR overestimated light to moderate precipitation (1–20mm/day) and underestimated very heavy precipitation (>50mm/day). TRMM3B42 and CoSch depicted behaviors similar to the surface observations; however, CoSch underestimated the precipitation in very intense systems (>50mm/day).The statistical variables indicated that CoSch's correlation coefficient was highest for every season and basin. Additionally, the bias and RMSE values suggested that CoSch closely represented the surface observations

A surrogate model for studying random field energy release rates in 2D brittle fractures

This article proposes a weighted-variational model as an approximated surrogate model to lessen numerical complexity and lower the execution times of brittle fracture simulations. Consequently, Monte Carlo studies of brittle fractures become possible when energy release rates are modelled as a random field. In the weighed-variational model, we propose applying a Gaussian random field with a Mat\'ern covariance function to simulate a non-homogeneous energy release rate ($G_c$) of a material. Numerical solutions to the weighed-variational model, along with the more standard but computationally demanding hybrid phase-field models, are obtained using the FEniCS open-source software. The results have indicated that the weighted-variational model is a competitive surrogate model of the hybrid phase-field method to mimic brittle fractures in real structures. This method reduces execution times by 90\%. We conducted a similar study and compared our results with an actual brittle fracture laboratory experiment. We present an example where a Monte Carlo study is carried out, modeling $G_c$ as a Gaussian Process, obtaining a distribution of possible fractures, and load-displacement curves