Luis Mediero

Changing climate shifts timing of European floods

Flooding along the river Will a warming climate affect river floods? The prevailing sentiment is yes, but a consistent signal in flood magnitudes has not been found. Blöschl et al. analyzed the timing of river floods in Europe over the past 50 years and found clear patterns of changes in flood timing that can be ascribed to climate effects (see the Perspective by Slater and Wilby). These variations include earlier spring snowmelt floods in northeastern Europe, later winter floods around the North Sea and parts of the Mediterranean coast owing to delayed winter storms, and earlier winter floods in western Europe caused by earlier soil moisture maxima. Science, this issue p. 588 see also p. 552 Climate change is affecting the timing of river flooding across Europe. A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale.

Diagnosing Causes of Water Scarcity in Complex Water Resources Systems and Identifying Risk Management Actions

From the water management perspective, water scarcity is an unacceptable risk of facing water shortages to serve water demands in the near future. Water scarcity may be temporary and related to drought conditions or other accidental situation, or may be permanent and due to deeper causes such as excessive demand growth, lack of infrastructure for water storage or transport, or constraints in water management. Diagnosing the causes of water scarcity in complex water resources systems is a precondition to adopt effective drought risk management actions. In this paper we present four indices which have been developed to evaluate water scarcity. We propose a methodology for interpretation of index values that can lead to conclusions about the reliability and vulnerability of systems to water scarcity, as well as to diagnose their possible causes and to propose solutions. The described methodology was applied to the Ebro river basin, identifying existing and expected problems and possible solutions. System diagnostics, based exclusively on the analysis of index values, were compared with the known reality as perceived by system managers, validating the conclusions in all cases.

Quantitative Assessment of Climate Change Vulnerability of Irrigation Demands in Mediterranean Europe

This paper presents an analysis of water resources management under climate change in Southern European River Basin Districts. The analysis is based on the Water Availability and Adaptation Policy Analysis (WAAPA) model, which focuses on the quantitative evaluation of maximum potential water withdrawal for different types of demands. The Water Availability and Adaptation Policy Analysis model performs the simulation of water resources systems at the monthly time scale and allows the estimation of the demand-reliability curve in every subbasin of the river network. Over sixty River Basin Districts of Southern Europe have been analyzed, taking basic information from publicly available databases: basin topology from the Hydro1K database, average runoff from the University of New Hampshire Global Runoff Data Centre (GRDC) composite runoff field, population from the Global Rural–urban Mapping Project (GRUMP) and irrigation area from the Global Map of Irrigated Area dataset. Streamflow monthly time series were obtained from the results of the ENSEMBLES project in four climate scenarios for time horizon 2070–2100. Climate change vulnerability of irrigation demands is estimated from changes in maximum potential water withdrawals for irrigation in current and future scenarios. Maximum potential water withdrawal for irrigation was computed as the largest value of irrigation demand that could be supplied with a given reliability requirement once the existing urban demand is adequately satisfied. The results show significant regional disparities in vulnerability to climate change in the irrigation sector across Europe. The greatest vulnerabilities have been obtained for Southwest Europe (Iberian Peninsula) and some basins in Italy and Greece.

A probabilistic model to support reservoir operation decisions during flash floods

Abstract A probabilistic model to assist decision makers in selecting the best reservoir operation strategy during flash floods is presented, based on Bayesian networks calibrated with the results of a rainfall—runoff model coupled with a reservoir operation model. During real-time operation, rainfall recorded in the basin is used to make probabilistic predictions of inflow discharge into the reservoir with a rainfall—runoff Bayesian network. The reservoir Bayesian network takes these probabilistic discharge values as input data and gives the probabilistic outflow discharge and water level at future time steps for the different operation strategies considered. From these probabilistic results, the best strategy for the operation of the floodgate can be selected in terms of the probability of maximum discharge downstream of the reservoir and risk of damage to the dam. Two data sets of 4000 inflow hydrographs were obtained through Monte Carlo simulation with a rainfall—runoff model and a reservoir management model. The Bayesian networks learned from the first data set and were validated with the second one. The methodology was tested successfully for one reservoir located in the south of Spain with observed data recorded during a recent flood event, checking its usefulness as a decision-making tool in real-time reservoir management.

Characterisation of the Sensitivity of Water Resources Systems to Climate Change

This paper offers a methodology that enables characterisation of the behaviour of water resources systems under the impact of climate change through assessment of sensitivity patterns in a wide range of hydrologic variations produced by such change. Analysis is based on the application of two indicators that, in turn, draw on the results of a system optimisation model. Under this methodology the potential sensitivity of water resources systems in the cases of different climate projections are visualised, allowing those systems that require special attention in their adaptation to climate change to be identified. The methodology is applied to three basins located in Spain: Guadalquivir, Ebro and the Spanish part of the international basin Duero.

Probabilistic calibration of a distributed hydrological model for flood forecasting

Abstract The complexity of distributed hydrological models has led to improvements in calibration methodologies in recent years. There are various manual, automatic and hybrid methods of calibration. Most use a single objective function to calculate estimation errors. The use of multi-objective calibration improves results, since different aspects of the hydrograph may be considered simultaneously. However, the uncertainty of estimates from a hydrological model can only be taken into account by using a probabilistic approach. This paper presents a calibration method of probabilistic nature, based on the determination of probability functions that best characterize different parameters of the model. The method was applied to the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model using the Manzanares River basin in Spain as a case study. The proposed method allows us to consider the uncertainty in the model estimates by obtaining the probability distributions of flows in the flood hydrograph. Citation Mediero, L., Garrote, L. & Martín-Carrasco, F. J. (2011) Probabilistic calibration of a distributed hydrological model for flood forecasting. Hydrol. Sci. J. 56(7), 1129–1149.

Extension of observed flood series by combining a distributed hydro-meteorological model and a copula-based model

Long flood series are required to accurately estimate flood quantiles associated with high return periods, in order to design and assess the risk in hydraulic structures such as dams. However, observed flood series are commonly short. Flood series can be extended through hydro-meteorological modelling, yet the computational effort can be very demanding in case of a distributed model with a short time step is considered to obtain an accurate flood hydrograph characterisation. Statistical models can also be used, where the copula approach is spreading for performing multivariate flood frequency analyses. Nevertheless, the selection of the copula to characterise the dependence structure of short data series involves a large uncertainty. In the present study, a methodology to extend flood series by combining both approaches is introduced. First, the minimum number of flood hydrographs required to be simulated by a spatially distributed hydro-meteorological model is identified in terms of the uncertainty of quantile estimates obtained by both copula and marginal distributions. Second, a large synthetic sample is generated by a bivariate copula-based model, reducing the computation time required by the hydro-meteorological model. The hydro-meteorological modelling chain consists of the RainSim stochastic rainfall generator and the Real-time Interactive Basin Simulator (RIBS) rainfall-runoff model. The proposed procedure is applied to a case study in Spain. As a result, a large synthetic sample of peak-volume pairs is stochastically generated, keeping the statistical properties of the simulated series generated by the hydro-meteorological model. This method reduces the computation time consumed. The extended sample, consisting of the joint simulated and synthetic sample, can be used for improving flood risk assessment studies.

Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization

This paper presents a methodology to achieve the identification of optimal hedging rules for operating reservoir systems, seeking to mitigate the drought impacts. The heuristic Particle Swarm Optimization (PSO) method is adopted as the optimization solver. This procedure establishes a two-phase method that combines PSO with the simulation of the water system, representing a system of reservoirs that are jointly operated to satisfy a set of demands with different priorities. The hedging rules are based on monthly storage levels that trigger restrictions on the demands. As model parameters, monthly rule activation thresholds and rationing factors were used for each type of demand. The optimization procedure minimizes an objective function that penalizes large deficits and assigns different weights to different demand types. Since the whole problem is quite complex, its dimensionality is reduced through: i) a set of candidate monthly activation thresholds are selected a priori associated to given risk conditions; and ii) the rationing factors are defined for every demand of each threshold throughout all months. In addition, an effort is made to avoid the trap in local optimums, whilst several other comments considering the application of the PSO method in the examined applications are provided. The procedure has been successfully applied to four water resource systems in Spain. From the application it can be seen that the deficits of the water supply demand are nearly removed, thanks to the larger weight given to the deficits of this demand type. The irrigation deficits are also reduced, since we lead to a sequence of smaller shortages than only one potential catastrophic shortage.

Trends in low flows in Spain in the period 1949–2009

ABSTRACT This study analyses trends in low flows in Spain in the period 1949–2009, based on daily flow data collected at 60 gauging stations located in near-natural catchments. Two low-flow indicators were considered: (i) the seven-day annual minimum streamflow and (ii) the 10th percentile of the yearly flow duration curve. Catchments were clustered into three regions in terms of monthly mean flows. The Mann-Kendall test was used considering four periods between 1949 and 2009. A multi-temporal trend analysis was also applied to the longest series to identify wet and dry periods that could influence the results. Lastly, a field significance test provided a regional assessment of the at-site detected trends at each region. The results for each indicator reveal a clearly decreasing trend in low flows throughout the northern half of Spain that was found to be field-significant over the (Atlantic and Mediterranean) regions. Editor Z.W. Kundzewicz; Associate editor not assigned

Learning Bayesian Networks from Deterministic Rainfall-Runoff Models and Monte Carlo Simulation

A mixed approach based on the combination of deterministic physically based models and probabilistic data-driven models for flood forecasting is presented. The approach uses a Bayesian network built upon the results of a deterministic rainfall–runoff model for real-time decision support. The data set for the calibration and validation of the Bayesian model is obtained through a Monte Carlo simulation technique, combining a stochastic rainfall generator and a deterministic rainfall–runoff model. The methodology allows making probabilistic discharge forecasts in real time using an uncertain quantitative precipitation forecast. The validation experiments made show that the data-driven model can approximate the probability distribution of future discharge that would be obtained with the physically based model applying ensemble prediction techniques, but in a much shorter time.