Wavelet Analysis of Rainfall and Runoff Multidecadal Time Series on Large River Basins in Western North Africa

This work is dedicated to the study of the spatio-temporal variability of climate in Morocco by the analysis of rainfall (gridded and gauged data) and runoff. The wavelet analysis method has been used in this study to compare the rainfall and runoff series and to show the major discontinuities identified in 1970, 1980, and 2000. Several modes of variability have been detected; this approach has been applied to show annual (1 year) and inter-annual modes (2–4 years, 4–8 years, 8–12/8–16 years, and 16–30 years), and some modes are specific to some stations. This analysis will be complemented by the gridded data covering the period from 1940 to 1999, which will allow for a better understanding of the spatial variability of the highlighted signals set, which identified frequencies at 1 year and 8–16 years, distinguished different time periods at each basin and identified three main discontinuities in 1970, 1980, and 2000. The contribution of climatic indices is important as it is between 55% and 80%.

Adaptation and Application of the large LAERTES-EU RCM Ensemble for Modeling Hydrological Extremes: A pilot study for the Rhine basin

Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12.000 simulated years. LAERTES-EU is adapted for the use in an HM to calculate discharges for large river basins by applying a quantile mapping with a fixed density function to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in Central Europe.

PAISAGENS FLUVIAIS E PEIXES DE RIACHO: UMA INTRODUÇÃO AOS DADOS, UNIDADES ESPACIAIS DE ANÁLISE E CLASSIFICAÇÃO

Research on riverscapes and stream fish ecology has undergone a wide progress since the 1990s. Several conditions have been pointed out as essential in this progress, including the following four: (a) availability of geo-technology and spatial data, (b) setting the regional context of study areas (biomes, large river basins, ecoregions); (c) defining hierarchical spatial units of analysis (watersheds, segments, reaches) and their attributes (e.g., area, slope, order, perennial/intermittent), and (d) classification of spatial units according to their attributes. Here we present an introduction to these topics, using examples from studies in Brazilian streams, where research progress on riverscapes and stream fish has occurred only more recently. We identify a few challenges in the Brazilian context, including the standardization and consolidation of regional and national spatial databases that support riverscape analyses, training fish ecologists in Geographic Information Systems (GIS) and spatial data, expanding the use of classification systems in different spatial coverages and resolutions, research on structural, spatial and temporal attributes of spatial units in riverscape analyses

Predicting Outflow Hydrographs of Potential Dike Breaches in a Bifurcating River System Using NARX Neural Networks

Early flood forecasting systems can mitigate flood damage during extreme events. Typically, the effects of flood events in terms of inundation depths and extents are computed using detailed hydraulic models. However, a major drawback of these models is the computational time, which is generally in the order of hours to days for large river basins. Gaining insight in the outflow hydrographs in case of dike breaches is especially important to estimate inundation extents. In this study, NARX neural networks that were capable of predicting outflow hydrographs of multiple dike breaches accurately were developed. The timing of the dike failures and the cumulative outflow volumes were accurately predicted. These findings show that neural networks—specifically, NARX networks that are capable of predicting flood time series—have the potential to be used within a flood early warning system in the future.