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The study presents a method which can be used to define real-time operation rules for gated spillways (named the K-Method). The K-Method is defined to improve the performance of the Volumetric Evaluation Method (VEM), by adapting it to the particular conditions of the basin, the reservoir, or the spillway. The VEM was proposed by the Spanish engineer Fernando Giron in 1988 and is largely used for the specification of dam management rules during floods in Spain. This method states that outflows are lower than or equal to antecedent inflows, outflows increase when inflows increase, and the higher the reservoir level, the higher the percentage of outflow increase. The K-Method was developed by modifying the VEM and by including a K parameter which affects the released flows. A Monte Carlo environment was developed to evaluate the method under a wide range of inflow conditions (100,000 hydrographs) and with return periods ranging from one to 10,000 years. The methodology was applied to the Talave reservoir, located in the South-East of Spain. The results show that K-values higher than one always reduce the maximum reservoir levels reached in the dam. For K-values ranging from one to ten, and for inflow hydrographs with return periods higher than 100 years, we found a decrease in the maximum levels and outflows, when compared to the VEM. Finally, by carrying out a dam risk analysis, a K-value of 5.25 reduced the expected annual damage by 8.4% compared to the VEM, which represents a lowering of 17.3% of the maximum possible reduction, determined by the application of an optimizer based on mixed integer linear programming (MILP method).

We identified and analysed droughts in the La Plata Basin (divided into seven sub-basins) for the current period (1961-2005) and estimated their expected evolution under future climate projections for the periods 2011-2040, 2041-2070, and 2071-2099. Future climate projections were analysed from results of the Eta Regional Climate Model (grid resolution of approximately 10 km) forced by the global climate model HadGEM2-ES over the La Plata basin, and considering a RCP4.5 emission scenario. Within each sub-basin, we particularly focused our drought analyses on croplands and grasslands, due to their economic relevance. The three-month Standardized Precipitation Evapotranspiration Index (SPEI3) was used for drought identification and characterization. Droughts were evaluated in terms of time (percentage of time from the total length of each climate scenario), space (percentage of total area), and severity (SPEI3 values) of cells characterized by cropland and grassland for each sub-basin and climate scenario. Drought-severity-area-frequency curves were developed to quantitatively relate the frequency distribution of drought occurrence to drought severity and area. For the period 2011-2040, droughts dominate the northern sub-basins, whereas alternating wet and short dry periods dominate the southern sub-basins. Wet climate spread from south to north within the La Plata Basin as more distant future scenarios were analysed, due to both a greater number of wet periods and fewer droughts. The area of each sub-basin affected by drought in all climate scenarios was highly varied temporally and spatially. The likelihood of the occurrence of droughts differed significantly between the studied cover types in the Lower Paraguay sub-basin, being higher for cropland than for grassland. Mainly in the Upper Paraguay and in the Upper Paraná basins the climate projections for all scenarios showed an increase of moderate and severe droughts over large regions dedicated to crops and grasses. On the other hand, for the near future, the Lower Uruguay and the River Plata basins showed a decrease of drought severity compared to the current period. Projections suggest an increase in competition among uses in these regions and the need for a potential relocation of certain crops from the northern regions towards cooler regions located in the centre and south. Further research should consider other climate projections and perform high spatial resolution studies in localized areas.

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.

Pressure management (PM) is commonly used in water distribution systems (WDSs). In the last decade, a strategic objective in the field has been the development of new scientific and technical methods for its implementation. However, due to a lack of systematic analysis of the results obtained in practical cases, progress has not always been reflected in practical actions. To address this problem, this paper provides a comprehensive analysis of the most innovative issues related to PM. The methodology proposed is based on a case-study comparison of qualitative concepts that involves published work from 140 sources. The results include a qualitative analysis covering four aspects: (1) the objectives yielded by PM; (2) types of regulation, including advanced control systems through electronic controllers; (3) new methods for designing districts; and (4) development of optimization models associated with PM. The evolution of the aforementioned four aspects is examined and discussed. Conclusions regarding the current status of each factor are drawn and proposals for future research outlined

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, some of these models assume linear relationships between variables and prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks (BNs) were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharge as a result. The proposed BN model can be applied to supply the estimation uncertainty in national flood discharge mappings. The methodology was applied to a case study in the Tagus basin in Spain.

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 overtopping.

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.

Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.

A distributed model for real-time rainfall-runoff simulation during floods is presented. The model, called Distributed Basin Simulator (DBSIM) is based on the detailed topographical information provided by Digital Elevation Models (DEM). Basin representation uses the rectangular grid of the DEM. Soil properties, input data and state variables are also represented as data layers using the same scheme. Distributed rainfall input is used to map the topographically driven evolution of saturated areas as the storm progresses. The model applies a kinematic model of infiltration to evaluate local runoff generation in grid elements and accounts for lateral moisture flow between elements and surface flow routing in a simplified manner. Model performance is evaluated through sensitivity analyses and an exercise of model calibration and verification.

The software environment developed to use a distributed rainfall-runoff model in real time is described. The package, called Real-time Interactive Basin Simulator (RIBS), manages the real-time operation of the simulation model, providing several modes of user access to data, results and intermediate states. The user interface can generate hydrographs at any point within the basin, display the time evolution of model variables for any grid element, or represent the spatial distribution of basic or derived variables.

A model of infiltration, subsurface lateral flow and surface runoff generation that considers the effects of anisotropy and vertical heterogeneity is developed. The flow relations are based on the kinematic approximation and account for the influence of terrain slope. The model consists of equations for the evolution of the wetting front, the growth of the perched saturation zone, and the rate of unsaturated and saturated lateral subsurface flow.

La tesis presenta dos entornos de representación del conocimiento, SIM y CYRAH, que pueden utilizarse como ayuda a la decisión en tiempo real para previsión de avenidas en las cuencas mediterráneas. El proceso de toma de decisiones durante una inundación pasa por determinas etapas en las que se superan las posibilidades que actualmente ofrecen los modelos matemáticos clásicos de cálculo hidrológico. La toma de decisiones normalmente tiene lugar en un ambiente profesional en el que la incertidumbre impone limitaciones de tipo práctico a la utilización de modelos de simulación. Los sistemas basados en el conocimiento ofrecen la posibilidad de representar los verdaderos razonamientos y juicios de valor que realizan los expertos para construir un modelo que incorpore el conocimiento profesional que generalmente se aplica en estas situaciones. En primer lugar se presenta un estudio general del proceso de toma de decisiones durante una inundación. La previsión de avenidas en la vertiente mediterránea está influenciada en gran medida por las características meteorológicas, topográficas y geomorfológicas de sus cuencas. Ríos cortos, pendientes fuertes, deforestación y lluvias torrenciales son las causas fundamentales de la respuesta rápida e intensa a las lluvias de la zona y las consiguientes inundaciones. Un sistema de previsión ha de cumplir requerimientos muy fuertes: ha de ofrecer respuesta inmediata con un cierto grado de exactitud bajo circunstancias muy exigentes, como la imprevisible evolución de la situación meteorológica o el desconocimiento del comportamiento físico de las cuencas. Debido a la necesidad de manejar conocimientos cualitativos y valoraciones sobre los fenómenos físicos, la ayuda a la decisión se plantea bajo un enfoque de sistema experto, y, en consecuencia, se presenta una metodología de construcción de modelos de este tipo. La representación adecuada de los procesos físicos obliga a adaptar con este fin el diseño de la base de conocimiento y el motor de inferencia del sistema experto. Por tanto, la arquitectura que se propone para estos sistemas basados en le conocimiento combina los esquemas de razonamiento profesional seguidos por los hidrólogos, como los modelos tradicionales de simulación, con los esquemas simbólicos de resolución de problemas, como las reglas y marcos. El sistema de ayuda a la decisión se concibe como un entorno inteligente que sea capaz de identificar automáticamente el escenario de problemas que se podría plantear consistente con el conjunto de datos que se recibe durante la inundación. La posibilidad de orientar la búsqueda de problemas con un modelo simplificado y posteriormente refinar y ajustar la valoración original utilizando el análisis numérico es una estrategia clásica de resolución de problemas en la ingeniería. Este enfoque es la base de la estructura de los entornos que se presentan, bajo el punto de vista global de la previsión de avenidas en tiempo real. Se presta una atención especial al hecho de que las entradas y el comportamiento del sistema físico sólo se conocen parcialmente y, por tanto, es necesario asumir un cierto grado de incertidumbre en los datos. Como resultado de la investigación se presentan dos modelos originales: SIM y CYRAH. Ambos son aplicaciones de tipo mixto, a mitad de camino entre los sistemas clásicos de simulación numérica, basados en modelos matemáticos, y los sistemas de inteligencia artificial, basados en representación simbólica del conocimiento y procesos de inferencia. Para representar el comportamiento del sistema físico es necesario asumir una línea básica de razonamiento, pero la mayor parte del conocimiento que incorporan los sistemas se pretende que sea declarativo; esto es, definido por el usuario para adaptarse a su forma peculiar de entender el proceso de la toma de decisiones. El entorno SIM (Simulación e Inferencia con Modelos) permite hacer simulaciones del comportamiento de las cuencas a partir del conocimiento que el usuario tiene de ellas. Para ello representa el razonamiento sobre el comportamiento de un sistema físico mediante dos modelos numéricos de simulación, SAVEL y REBOLSA, integrados en un entorno de representación del conocimiento, SAR. SIM constituye un entorno de simulación basado en el conocimiento, en el que el usuario propone los criterios con los que se deben gestionar con información parcial modelos de cálculo hidrológico que no están totalmente calibrados. El entorno CYRAH (Cálculo y Razonamiento Hidrológico) incorpora el módulo de simulación SIM a una línea básica de razonamiento sobre el proceso de la inundación: En una primera fase, se analiza la situación actual. El experto es capaz de centrar su atención únicamente en los casos que son o podrían llegar a ser problemáticos en el futuro. Su conocimiento de los episodios registrados en el pasado le permite hacer conjeturas sobre evoluciones verosímiles de la situación meteorológica y evaluar aproximadamente la respuesta de las distintas cuencas a las lluvias esperables. En una segunda fase, se utiliza el entorno SIM para inferir los caudales y niveles futuros. La incertidumbre en los datos y en los parámetros que describen el comportamiento físico se supera repitiendo las simulaciones con combinaciones representativas de las entradas e interpretando los distintos resultados que se obtienen. En una tercera fase se estima a partir de los caudales y niveles obtenidos la posibilidad de presentación de problemas en un futuro próximo. The thesis presents two knowledge representation environments: SIM and CYRAH, which are to be used for real-time decision support in flood forecasting in Mediterranean basins. The decision making during a flood event involves a number of steps that clearly exceed the possibilities which are currently offered by classical mathematical hydrologic models. The decision usually takes place in a professional environment where uncertainty sets practical limitations to the use of simulation models. Knowledge based systems offer the possibility of representing the actual reasoning and judgements made by the experts to build a model that embodies the professional knowledge usually applied in these cases. First, a general approach for decisión support systems during floods is presented. Flood forecasting in the Mediterranean área is greatly influenced by the meteorological, topographical and geomorphological features of its watersheds. Short rivers, steep slopes, deforestation and torrential rains are the main causes for a fast and intense response to rainfall in the zone and the consequent flood. A warning system must meet strong requirements: prompt response of relative accuracy under demanding conditions, such as unpredictable meteorological situations or ignorance of the basins' physical behaviour. An expert system approach is proposed as an aid in decisión making, due to the need for handling qualitative knowledge and criteria about physical phenomena. A methodology for building this kind of knowledge-based systems is presented. Physical processes should be properly represented, and thus the knowledge base and inference engine of this type of expert system must be specially adapted to meet this requirement. Therefore, the proposed architecture for these knowledge based systems integrates the professional reasoning paradigms used by hydrologists, such as traditional simulation models, together with symbolic problem solving paradigms such as rules and frames. The decisión support system is conceived as an inteUigent environment capable of automatically identifying the problems that might occur consistently with the set of data received during a flood event. The ability to direct the search for problems with a rough engineering model and then refine and adjust the evalualion using numerical analysis is a classical approach to problem solving in engineering. This approach is the basis for the overall structure of the environments within the scope of real time flood forecasting. Much emphasis is placed on the fact that the inputs and the physical behaviour of the system are only partially known and therefore the uncertainty on the data sould be assumed. Two original models are presented as an outcome of the research: SIM and CYRAH. Both are intermediate applications that stand between the classical numeric simulation systems, based on mathematical models, and the artificial intelligence systems, based on symbolic knowledge representation and inference procedures. In order to represent the behaviour of the physical system a reasoning line has to be assumed, but most of the knowledge handled by the environments is intended to be declarative; that is, defíned by the user to adapt to his way of understanding the decisión making process. The SIM environment (Simulación e Inferencia con Modelos) is capable of making knowledge-based simulations of the behaviour of the basins. It represents the reasoning about the behaviour of the physical system using two numeric simulation models: SAVEL and REBOLSA, integrated in a knowledge representation environment: SAR. SIM is a knowledge-based environment that lets the user define the criteria for handling ill-calibrated hydrological models under incomplete Information. The CYRAH environment (Cálculo Y RAzonamiento Hidrológico) integrales the simulation module SIM in a basic reasoning line about the flooding process: In a first phase, the current situation is analized. The expert is able to focus his attention only in the cases that are or might be problematic in the futiu"e. His knowledge of past events enables him to make conjectures on plausible evolutions of the meteorológica! situation and evalúate roughly the response in the different basins to future rains. As a result of his analysis, a limited number of cases are selected for further attention. In a second phase, the SIM environment is used lo estímate discharges and levéis in those cases. The uncertainty in the data and the descriptors of the physical structure is overeóme by repeating the simulations using representative combinations of inputs and inlerpreting the different outpuls obtained. The chances of problems arising in the near future are deduced in a third phase from the discharges and levéis estimaied.