Decision support systems for large dam planning and operation in Africa

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Inferring efficient operating rules in multireservoir water resource systems: A review

[EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. 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EFFECTIVE SEDIMENT CONTROL IN A RESERVOIR

Sedimentation in a reservoir cannot be avoided. The average rate of sedimentation on the storage volume reduction of a reservoir in the world is about 1 % per year (Yoon,1992), meanwhile, the storage volume reduction in several reservoir in Indonesia reaches 1,64% to 2,83% per year (Atmojo,2012). These sediment’s accumulations in the reservoir will continually reduce the storage volume, thus the intended functions of reservoirs for flood control (Atmojo, 2013), irrigation and water supply, electric generation, etc. will also reduced and not optimal. Some of sediment control measures have been practiced in reducing sediment accumulation in reservoirs around the world. In principle, there are two approaches i.e., reduce the sediment input to a reservoir by land conservation, construction of check dam, sand pocket, diversion channel, etc. and reduce the sedimentation in the reservoir by sluicing, turbidity current, dredging, and flushing (Morris and Fan, 1998; Emamgholizadeh et al., 2006). This paper presents the performance of sediment’s reduction from a reservoir by flushing, sluicing, and disturbing flushing based on some laboratories results (Atmojo,2012). It is expected that this paper can contribute to elicits some finding on the selection of which suitable method for sediment reduction from a reservoir

Estudio comparativo completo de varios métodos basados en datos para la gestión de los recursos hídricos en ambientes mediterráneos a través de diferentes escalas temporales

Since the beginning of time, there has been innovation in the knowledge and technology of water and the hydraulic systems, to achieve an efficient and upgrade management of them. In this project, as an opening hypothesis, we will apply computational techniques and Artificial Intelligence concepts. Given that the primary asset of these studies is data, we have preferred to use the term ”Data-Driven”, as the term Artificial Intelligence can cause confusion in non-experts. This is an expanding field in all aspects of science and life, where the computing and processing powers are increasing periodic, so does the generation of information. There we have 5G technology, or the Internet of things, where the exponential build up in the volume of data utilised, pushes us to set up frameworks for the treatment and analysis of the information.Data-Driven techniques offers enormous potential to transform our perception to understand,monitor and predict the states of hydro-meteorological variables. Its application provides benefits, however, performing these exercises requires practice and explicit knowledge. Therefore, a deeper understanding of the capabilities and limitations of novel computational techniques within our field of knowledge is needed. Hence, it is essential to carry out ”hydro-informatics” experiences under this assumption. For the development of these models, we identify which points are the most relevant and need to be taken into account in regional conditions or frameworks. In consequence, we will work with the time series collected in the different monitoring networks, selecting the hydrological points of interest, in order to further develop hydrological frameworks that are useful for water management and optimisation. Here, we are interested in seeing the practical applicability to hydro-meteorology under Mediterranean conditions, where data are sometimes scarce, by selecting two hydrographic basins in south-east Andalusia: the Guadalhorce river (Málaga) and the Guadalfeo river (Granada). In chapter 1, an introduction to the doctoral thesis is made. Likewise, we establish the general and the specific objectives, and the motivation of the thesis. Afterwards, we describe the three fundamental exercises to be carried out in the research work: Regression, Classification and Optimisation. Ultimately, we carry out a brief review of previous works under Mediterranean climatic conditions and similar assumptions. Chapter 2 presents the study areas, analysing the spatial and temporal characteristics of two Andalusian Mediterranean basins in south-east Spain: Guadalhorce (GH) and Guadalfeo (GF). These are hydrographic basins with highly variable/heterogeneous spacetime patterns. The first hydrological system, GH, contains an area of socio-economic importance, such is the city of M´alaga. The second, GF, to the north has the Sierra Nevada National Park, crowned by the Mulhac´en peak and flowing in a few kilometres into the area of Motril. In this particular water system, we find large gradients of the geophysical agents. Both systems have regulation structures of great interest for the development and study of their optimisation. We also review the monitoring networks available in these basins, and which environmental agents and/or processes should be taken into account to meet the objectives of this work. We carry out a bibliographic review of the most relevant historical floods, listing the factors associated with these extreme events. In the data analysis stage of this chapter, we focus on the spatialtemporal evolution of the risk of flooding in the two mouths of the Guadalhorce and Guadalfeo Rivers into the Albor´an Sea. We quantify that had stepped up in recent years, noting that dangerous practices have increased the risk of flooding because of the intrusion of land uses with high-costs. This chapter also analyses collected data within the monitoring networks, to understand the occurrence of floods in the river GH related to upstream discharges. We found that this basin has limitations in regulation and cannot mitigate costs downstream. The results got, were part of the work presented in Egüen et al. (2015). These analyses allow us to identify in which parts of the flood management of this hydrological system need a more precise optimisation. Finally, a summary of another important hydrological risk is carried out, such as droughts, and how these water deficits can be represented by standardised indices, both in rainfall and the flow rates. The various approaches and methodologies for hydro-meteorological time series modelling are discussed in the chapter 3. The contrasting concepts are exposed antagonistically, to focus on the different design choices that we need to make: black box vs. grey box vs. white box, parametric vs. non-parametric, static vs. dynamic, linear vs. non-linear, frequency vs. Bayesian, single vs. multiple, among others..., detailing the advantages and disadvantages of each approach. We presented some ideas that emerged in this part of the research in Herrero et al. (2014). The partition, management and data transformation steps for the correct application of these experimental methods are also discussed. This is of great importance, since part of the hard work in the application of these methods comes from the transformation of the data. So that, the algorithms and transfer functions work correctly. Finally, we focus on how to test and validate the deterministic and probabilistic behaviours through evaluative coefficients to avoid coefficients that mask the results, and therefore focus on the behaviours of our interest, in our case precision and predictability. We have also taken parsimony into account in models based on neural networks, since they can easily fall into over-parameterisation. In chapter 4, we present the experimental work, where seven short-term, six daily and one hourly rainfall-runoff regressions are performed. The case studies correspond to various points of interest within the study areas with important implications for hydrological management. On an hourly scale, we analyse the efficiency and predictive capacities of the MLR and BNN at ten time horizons for the level of the Guadalhorce River in Cártama. We found that, for closer predictive horizons, a simpler approach such as linear (MLR) can outperform other with a priori higher capabilities, such as non-linear (BNN). This finding could simplify greatly its development and application. At a daily scale, we establish a comparative framework between the two previous models and a complete Bayesian method such as the Gaussian Processes. This DD computational technique, allows us to apply different transfer functions under a single model. This is an advantage over the other two DD models, since the results show that they work well in one domain, but do not work well in the other. During the construction of the models, we do the selection of the input variables in a progressive way, through a trial-and-error method, where the significant improvements with respect to the last predictor structure are taken into account preserving the principle of parsimony. Here, we have used different types of data: real data collected in the monitoring networks, and data generated in parallel from physically based hydrological modelling (WiMMed). The results are robust, where the major limitation is the high computational cost by the recurrent and iterative method used. Some results of this chapter, were presented in Gulliver et al. (2014). In chapter 5 three medium-term time scale prediction experiments are performed. We base the first modelling experiment on a quarterly scale, where a hydrological time scheme determines the cumulative flow for specific time horizons. We start the scheme according to the relevant dates where hydrological planning takes place. It is validated that the forecasts are more prosperous after have been consumed the first six months of the hydrological year. Instead of the three months in which we carry out the evaluations. The observed input variables quantified in the water system are: cumulative stream flow, cumulative rainfall, cumulative snowfall values and atmospheric oscillations (AO). At the level of modelling with DD, this experience has shown the importance of combining mixed regression classification models instead of only regression models within static frameworks. In this manner, we reduce and narrow the space of possible solutions and, therefore, we optimised the predictive behaviour of the DD model. During the development of this exercise, we have also carried out a classification practice comparing three DD classifiers: Probabilistic Neural Network (PNN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). We see that the SVM behaves better than the others with our data. However, more research is still needed on classifiers in hydro-meteorological frameworks like ours, because of their variability. We showed this part of the doctoral thesis in Gulliver et al. (2016). In the second section of this chapter (Sec. 5.3), we carry out a rain forecast exercise on a monthly scale. To do so, we use BNN following the same construction method of the SVI model exposed in the previous chapter (Sec. Ref. Chapter 4), thus validating it in another time scale. However, the results in predictive terms are poor for this hydro-meteorological variable. This confirms the difficulty of predicting this variable from historical data and without the incorporation of dynamic tools. Thus, the need for complex hydrodynamic modelling for the prediction of this important variable is confirmed. On the other hand, this case serves to empirically infer the causality of the most relevant atmospheric oscillations in the points of study. From multiple simulations with the model-based approach it has been possible to establish which indices have a greater influence. In the last section of this chapter (Section 5.4), an exercise was carried out to predict the deviation or anomaly of rainfall and runoff indices for four time series representative of different locations within the Guadalfeo BR. In this case, we verified the suitability of seven statistical distributions to characterize the anomalies/deviations under Mediterranean conditions. Under this hypothesis, the indices that passed the Shapiro-Wilk normality test were modelled to analyse the capabilities of BNN to predict these indices at various time horizons. Here, predictions of negative phases (droughts or deficit periods) have been poor, and the behaviour of the models for positive phases (wet periods) has been more successful. Regarding the causal inference of IC and its possible influence on the study area, we found out how NAO and WEMO help forecasts for shorter time horizons, while MOI helps for longer cumulative time horizons/times. We have analysed the relevance of these atmospheric variables in each case where sometimes their introduction was convenient and sometimes not, following the rules of construction and detailing them in each case study. Throughout the work, the usefulness of mixed modelling approaches has been verified, using models based on observed data from the different monitoring networks with physical modelling for the reproduction of essential hydrological processes. With the proposed methodology, a positive influence of atmospheric oscillations has been observed for medium-term prediction within the study regions, finding no evidence for short-term predictions (daily scale). The final conclusions and the most important points for future work are presented in the chapter 6. Applications of this type of methods are currently necessary. They help us to establish relationships based on measured hydro-meteorological data and thus ”based on real data”, without hypothesizing any assumptions. These data-based experiences are very useful for limiting future uncertainty and optimizing water resources. The establishment of temporal relationships between different environmental agents allows us, through supervised methods, to establish causal relationships. From here a physical inference exercise is necessary to add coherence and establish a robust scientific exercise. The results obtained in this work, reaffirm the practicality of implementing this Data- Driven frameworks, in both the public and private spheres, being a good starting point for technology transfer. Most of the routines and models provided in this thesis, could be directly applied in Hydro-meteorological Services, or Decision Support Systems for water officials. This includes potential users as varied as public administrations and basin organisations, reservoir managers, energy companies that manage hydroelectric generation, irrigation communities, water bottling plants,... etc. The establishment of iterative and automatic frameworks for data processing and modelling, needs to be implemented, to make the most of the data collected in the water systems.Desde el inicio de los tiempos, se innova en el conocimiento y la tecnología de los sistemas hídricos e hidráulicos con el fin de conseguir una eficiente y correcta gestión de los mismos. En este proyecto, como hipótesis de partida, se van a aplicar diversas técnicas computacionales y conceptos de Inteligencia Artificial. Dado que el principal activo de estas aplicaciones son los datos, optamos por el término ”Data-Driven” (DD), ya que el término de Inteligencia Artificial puede causar confusión en los no expertos. Este es un campo en expansión en todos los aspectos de la ciencia y de la vida, donde al tiempo que se incrementan las capacidades de computación y de procesamiento, se incrementa la generación de datos. Ahí tenemos la tecnología 5G, o el internet de las cosas, donde el incremento exponencial del volumen de datos que se utilizan nos obliga a desarrollar marcos para el tratamiento y el análisis de los mismos. Los métodos DD tienen un enorme potencial para transformar nuestra habilidad de establecer un seguimiento supervisado y predecir estados de variables hidro-meteorológicas. Su aplicación provee claramente de beneficios, sin embargo realizar estos ejercicios requiere una práctica y un conocimiento específico. Por ello, es necesario un entendimiento más profundo de las capacidades y de las limitaciones de estas técnicas computacionales, dentro de nuestro campo de conocimiento y casos específicos. Por estos motivos, es esencial realizar experiencias ”hidro-informáticas” bajo este supuesto, identificando así que puntos son los más relevantes y a tener en cuenta en el desarrollo y la validación de estos modelos en condiciones o marcos más regionales. Para ello, trabajaremos con las series temporales recogidas en las diferentes redes de monitorización, con series resultantes de modelado hidro-meteorológico y con series de las oscilaciones atmosféricas más relevantes en la zona de estudio. El objetivo principal de este trabajo es el desarrollo y la validación de marcos metodológicos basados en datos. Para ello, se seleccionan puntos de interés, con el fin de desarrollar marcos hidro-meteorológicos ´útiles en la gestión y optimización de los recursos hídricos. En este supuesto, nos interesa ver la aplicabilidad práctica de estas herramientas de aprendizaje automático, machine learning, en condiciones mediterráneas y locales, donde los datos a veces son escasos o de baja calidad. En el primer capítulo (Cap.1) se realiza una introducción a la tesis doctoral, estableciendo los objetivos tanto generales como específicos, y la motivación de la tesis. Seguidamente se realiza a modo introductorio una descripción de los tres ejercicios fundamentales a realizar en el trabajo de investigación: Regresión, Clasificación y Optimización. Finalmente, se realiza una revisión del estado del arte de trabajos previos bajo condiciones climáticas mediterráneas y similares. El capítulo 2 presenta las zonas de estudio, analizando las características espacio-temporales de dos cuencas mediterráneas andaluzas situadas en el sureste español: río Guadalhorce (GH) y río Guadalfeo (GF). Son cuencas hidrográficas con unos patrones espaciotemporales altamente variables/heterogéneos. El primer sistema hidrológico, GH, contiene una zona de gran importancia socio-económica como es la ciudad de Málaga. El segundo, GF, al norte tiene situado el Parque Nacional de Sierra Nevada, coronado por el pico Mulhacén y desemboca a pocos kilómetros en la costa de Motril. Esto hace que este sea un sistema con grandes gradientes geo-morfológicos e hidro-meteorológicos. En ambas cuencas existen estructuras de regulación de gran interés para el desarrollo y estudio de su optimización. También se revisan las redes de monitorización disponibles en estas cuencas, y que agentes deben ser tenidos en cuenta para la consecución de los objetivos del presente trabajo. En la etapa de análisis de datos de este capítulo, nos centramos en la evolución espacio temporal del riesgo frente a las inundaciones en las desembocaduras de ambos sistemas hidrológicos al mar de Alborán. Se cuantifica el aumento del riesgo frente a inundaciones ante la intrusión de usos del suelo con altos costes en las zonas potencialmente inundables en estos ´últimos años, constatando así una mala práctica en la planificación del territorio dentro de la zona de estudio. También, en este capítulo se analizan los datos registrados con el fin de comprender la ocurrencia de avenidas en el río GH y su relación con los desembalses aguas arriba. En este análisis se pudo identificar, como ante algunos eventos pluviométricos extremos (> 100mm/24h), esta cuenca tiene limitaciones en la regulación, no pudiendo así mitigar los costes aguas abajo. Parte de los resultados obtenidos formaron parte del trabajo presentado en Egüen et al. (2015). Estos análisis nos permiten identificar la necesidad de una optimización temporal más precisa en la gestión de avenidas en este sistema hidrológico. Finalmente, realizamos un análisis de otro riesgo hidrológico importante como son las sequías, y cómo podemos representar este déficit hídrico mediante índices estandarizados, tanto para la pluviometría como para la escorrentía. En el capítulo 3 se analizan los diversos enfoques y metodologías para el modelado de series temporales hidro-meteorológicas. Los enfoques se exponen de forma antagonista entre las diferentes opciones de modelado que tenemos: caja negra vs. caja gris vs. caja blanca, paramétricos vs. no-paramétricos, estático vs. dinámico, lineal vs. no-lineal, frecuentista vs. bayesiano, único vs múltiple, entre otros..., enumerando las ventajas e inconvenientes de cada enfoque. Algunas ideas surgidas en esta parte de la investigación fueron expuestas en Herrero et al. (2014). Por otro lado, también se discuten los pasos de partición, gestión y transformación de los datos para una correcta aplicación de este tipo de métodos experimentales. Esto es de gran importancia, ya que parte del trabajo duro en la aplicación de este tipo de metodologías, proviene de la transformación de los datos para que los algoritmos y las funciones de transferencia funcionen correctamente. En la parte final de este capítulo, nos centramos en cómo evaluar y validar el comportamiento determinista y probabilístico mediante coeficientes evaluativos. En este punto, prestamos especial atención en evitar la utilización de coeficientes que enmascaren los resultados o muy generalistas, y por lo tanto nos centramos en aquellos que evalúan las capacidades predictivas y de precisión de los modelos. También se ha tenido en cuenta la parsimonia para los modelos basados en redes neuronales, ya que pueden caer fácilmente en una sobre-parametrización. El capítulo 4 expone trabajo puramente experimental, donde se realizan siete regresiones lluvia escorrentía a corto plazo, seis diarias y una horaria. Los casos de estudio corresponden a diversos puntos de interés dentro de las zonas de estudio, con importantes implicaciones en la gestión hidrológica. A escala horaria se analiza las capacidades de eficiencia y predictivas de la Regresión Lineal Múltiple (MLR) y Redes Neuronales Bayesianas (BNN) a diez horizontes temporales para el nivel del río Guadalhorce en el puente de Cártama. Se encontró que, para horizontes predictivos más cercanos, un enfoque más sencillo como puede ser el lineal (MLR), puede superar a uno con mayores capacidades predictivas a priori, como pueden ser uno no lineal (BNN). Simplificando así, el desarrollo y la implementación de este tipo de técnicas computacionales bajo este tipo de marcos hidrológicos. Por otro lado, a escala diaria se establece un marco comparativo entre los dos modelos anteriores, MLR y BNN, y un método bayesiano completo: Procesos Gaussianos (GP). Esta técnica computacional, nos permite aplicar funciones de transferencia de diferente naturaleza bajo un único modelo. Esto es una ventaja con respecto a los otros dos modelos computacionales, ya que los resultados nos indican que a veces funcionan bien en un dominio, pero no funcionan bien en el contrario. Durante la construcción de los modelos, la selección de las variables de entrada se realiza de forma progresiva, mediante un método de prueba y error, donde se tienen en cuenta las mejoras significativas con respecto a la última estructura de predictores preservando el principio de parsimonia. Se han utilizado datos de diferente naturaleza: datos reales recogidos en las redes de monitorización y datos generados paralelamente de modalización hidrológica con base física (WiMMed). Los resultados son robustos donde la principal limitación es el alto coste computacional por el método recurrente e iterativo. Resultados de este capítulo fueron presentados en Gulliver et al. (2014). En el capítulo 5 se realizan tres

Assessment and implementation of evolutionary algorithms for optimal management rules design in water resources systems

Tesis por compendioWater is an essential resource from an environmental, biological, economic or social point of view. In basin management, the irregular distribution in time and in space of this resource is well known. This issue is worsened by extreme climate conditions, generating drought periods or flood events. For both situations, optimal management is necessary. In one case, different water uses should be supplied efficiently using the available surface and groundwater resources. In another case, the most important goal is to avoid damages in flood areas, including the loss of human lives, but also to optimize the revenue of energy production in hydropower plants, or in other uses. The approach presented in this thesis proposes to obtain optimal management rules in water resource systems. With this aim, evolutionary algorithms were combined with simulation models. The first ones, as optimization tools, are responsible for guiding the process iterations. In each iteration, a new management rule is defined in the simulation model, which is computed to comprehend the situation of the system after applying this new management. For testing the proposed methodology, four evolutionary algorithms were assessed combining them with two simulation models. The methodology was implemented in four real case studies. This thesis is presented as a compendium of five manuscripts: three scientific papers published in journals (which are indexed in the Journal Citation Report), another under review, and the last manuscript from Conference Proceedings. In the first manuscript, the Pikaia optimization algorithm was combined with the network flow SIMGES simulation model for obtaining four different types of optimal management rules in the Júcar River Basin. In addition, the parameters of the Pikaia algorithm were also analyzed to identify the best combination of them to use in the optimization process. In the second scientific paper, the multi-objective NSGA-II algorithm was assessed to obtain a parametric management rule in the Mijares River basin. In this case, the same simulation model was linked with the evolutionary algorithm. In the Conference manuscript, an in-depth analysis of the Tirso-Flumendosa-Campidano (TFM) system using different scenarios and comparing three water simulation models for water resources management was developed. The third published manuscript presented the assessment and comparison of two evolutionary algorithms for obtaining optimal rules in the TFM system using SIMGES model. The algorithms assessed were the SCE-UA and the Scatter Search. In this research paper, the parameters of both algorithms were also analyzed as it was done with the Pikaia algorithm. The management rules in the three first manuscripts were focused to avoid or minimize deficits in urban and agrarian demands and, in some case studies, also to minimize the water pumped. Finally, in the last document, two of the algorithms used in previous manuscripts were assessed, the mono-objective SCE-UA and the multi-objective NSGA-II. For this research, the algorithms were combined with RS MINERVE software to manage flood events in Visp River basin minimizing damages in risk areas and losses in hydropower plants. Results reached in the five manuscripts demonstrate the validity of the approach. In all the case studies and with the different evolutionary algorithms assessed, the obtained management rules achieved a better system management than the base scenario of each case. These results usually mean a decrease of the economic costs in the management of water resources. However, comparing the four algorithms assessed, SCE-UA algorithm proved to be the most efficient due to the different stop/convergence criteria and its formulation. Nevertheless, NSGA-II is the most recommended due to its multi-objective search focus on the enhancement of different objectives with the same importance where the decision makers can make the best decision for the management of the system.El agua es un recurso esencial desde el punto de vista ambiental, biológico, económico o social. En la gestión de cuencas, es bien conocido que la distribución del recurso en el tiempo y el espacio es irregular. Este problema se agrava debido a condiciones climáticas extremas, generando períodos de sequía o inundaciones. Para ambas situaciones, una gestión óptima es necesaria. En un caso, el suministro de agua a los diferentes usos del sistema debe realizarte eficientemente empleando los recursos disponibles, tanto superficiales como subterráneos. En el otro caso, el objetivo más importante es evitar daños en las zonas de inundación, incluyendo la pérdida de vidas humanas, pero al mismo tiempo, optimizar los beneficios de centrales hidroeléctricas, o de otros usos. El enfoque presentado en esta tesis propone la obtención de reglas de gestión óptimas en sistemas reales de recursos hídricos. Con este objetivo, se combinaron algoritmos evolutivos con modelos de simulación. Los primeros, como herramientas de optimización, encargados de guiar las iteraciones del proceso. En cada iteración se define una nueva regla de gestión en el modelo de simulación, que se evalúa para conocer la situación del sistema después de aplicar esta nueva gestión. Para probar la metodología propuesta, se evaluaron cuatro algoritmos evolutivos combinándolos con dos modelos de simulación. La metodología se implementó en cuatro casos de estudio reales. Esta tesis se presenta como un compendio de cinco publicaciones: tres de ellas en revistas indexadas en el Journal Citation Report, otra en revisión y la última como publicación de un congreso. En el primer manuscrito, el algoritmo de optimización Pikaia se combinó con el modelo de simulación SIMGES para obtener reglas de gestión óptimas en la cuenca del río Júcar. Además, se analizaron los parámetros del algoritmo para identificar la mejor combinación de los mismos en el proceso de optimización. El segundo artículo evaluó el algoritmo multi-objetivo NSGA-II para obtener una regla de gestión paramétrica en la cuenca del río Mijares. En el trabajo presentado en el congreso se desarrolló un análisis en profundidad del sistema Tirso-Flumendosa-Campidano utilizando diferentes escenarios y comparando tres modelos de simulación para la gestión de los recursos hídricos. En el tercer manuscrito publicado se evaluó y comparó dos algoritmos evolutivos (SCE-UA y Scatter Search) para obtener reglas de gestión óptimas en el sistema Tirso-Flumendosa-Campidano. En dicha investigación también se analizaron los parámetros de ambos algoritmos. Las reglas de gestión de estas cuatro publicaciones se enfocaron en evitar o minimizar los déficits de las demandas urbanas y agrarias y, en ciertos casos, también en minimizar el caudal bombeado, utilizando para ello el modelo de simulación SIMGES. Finalmente, en la última publicación se evaluó el algoritmo mono-objetivo SCE-UA y el multi-objetivo NSGA-II. Para esta investigación, los algoritmos se combinaron con el software RS MINERVE para gestionar los eventos de inundación en la cuenca del río Visp minimizando los daños en las zonas de riesgo y las pérdidas en las centrales hidroeléctricas. Los resultados obtenidos en las cinco publicaciones demuestran la validez del enfoque. En todos los casos de estudio y, con los diferentes algoritmos evolutivos evaluados, las reglas de gestión obtenidas lograron una mejor gestión del sistema que el escenario base de cada caso. Estos resultados suelen representar una disminución de los costes económicos en la gestión de los recursos hídricos. Comparando los cuatro algoritmos, el SCE-UA demostró ser el más eficiente debido a los diferentes criterios de convergencia. No obstante, el NSGA-II es el más recomendado debido a su búsqueda multi-objetivo enfocada en la mejora, con la misma importancia, de diferentes objetivos, donde los tomadores de decisiones pueden selL'aigua és un recurs essencial des del punt de vista ambiental, biològic, econòmic o social. En la gestió de conques, és ben conegut que la distribució del recurs en el temps i l'espai és irregular. Este problema s'agreuja a causa de condicions climàtiques extremes, generant períodes de sequera o inundacions. Per a ambdúes situacions, una gestió òptima és necessària. En un cas, el subministrament d'aigua als diferents usos del sistema ha de realitzar-se eficientment utilitzant els recursos disponibles, tant superficials com subterranis. En l'altre cas, l'objectiu més important és evitar danys en les zones d'inundació, incloent la pèrdua de vides humanes, però al mateix temps, optimitzar els beneficis de centrals hidroelèctriques, o d'altres usos. La proposta d'esta tesi és l'obtenció de regles de gestió òptimes en sistemes reals de recursos hídrics. Amb este objectiu, es van combinar algoritmes evolutius amb models de simulació. Els primers, com a ferramentes d'optimització, encarregats de guiar les iteracions del procés. En cada iteració es definix una nova regla de gestió en el model de simulació, que s'avalua per a conéixer la situació del sistema després d'aplicar esta nova gestió. Per a provar la metodologia proposada, es van avaluar quatre algoritmes evolutius combinant-los amb dos models de simulació. La metodologia es va implementar en quatre casos d'estudi reals. Esta tesi es presenta com un compendi de cinc publicacions: tres d'elles en revistes indexades en el Journal Citation Report, una altra en revisió i l'última com a publicació d'un congrés. En el primer manuscrit, l'algoritme d'optimització Pikaia es va combinar amb el model de simulació SIMGES per a obtindre regles de gestió òptimes en la conca del riu Xúquer. A més, es van analitzar els paràmetres de l'algoritme per a identificar la millor combinació dels mateixos en el procés d'optimització. El segon article va avaluar l'algoritme multi-objectiu NSGA-II per a obtindre una regla de gestió paramètrica en la conca del riu Millars. En el treball presentat en el congrés es va desenvolupar una anàlisi en profunditat del sistema Tirso-Flumendosa-Campidano utilitzant diferents escenaris i comparant tres models de simulació per a la gestió dels recursos hídrics. En el tercer manuscrit publicat es va avaluar i va comparar dos algoritmes evolutius (SCE-UA i Scatter Search) per a obtindre regles de gestió òptimes en el sistema Tirso-Flumendosa-Campidano. En dita investigació també es van analitzar els paràmetres d'ambdós algoritmes. Les regles de gestió d'estes quatre publicacions es van enfocar a evitar o minimitzar els dèficits de les demandes urbanes i agràries i, en certs casos, també a minimitzar el cabal bombejat, utilitzant per a això el model de simulació SIMGES. Finalment, en l'última publicació es va avaluar l'algoritme mono-objectiu SCE-UA i el multi-objetiu NSGA-II. Per a esta investigació, els algoritmes es van combinar amb el programa RS MINERVE per a gestionar els esdeveniments d'inundació en la conca del riu Visp minimitzant els danys en les zones de risc i les pèrdues en les centrals hidroelèctriques. Els resultats obtinguts en les cinc publicacions demostren la validesa de la metodología. En tots els casos d'estudi i, amb els diferents algoritmes evolutius avaluats, les regles de gestió obtingudes van aconseguir una millor gestió del sistema que l'escenari base de cada cas. Estos resultats solen representar una disminució dels costos econòmics en la gestió dels recursos hídrics. Comparant els quatre algoritmes, el SCE-UA va demostrar ser el més eficient a causa dels diferents criteris de convergència. No obstant això, el NSGA-II és el més recomanat a causa de la seua cerca multi-objectiu enfocada en la millora, amb la mateixa importància, de diferents objectius, on els decisors poden seleccionar la millor opció per a la gestió del sistema.Lerma Elvira, N. (2017). Assessment and implementation of evolutionary algorithms for optimal management rules design in water resources systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90547TESISCompendi

An Interactive Reservoir Management System for Lake Kariba

This paper presents a user-interactive decision support system (DSS) for the management of the Lake Kariba reservoir. Built in the fourth-generation computer language IFPS, the system takes into account relevant reservoir characteristics and parameters, such as the amount of hydropower generated, reservoir storage throughout the year, and the amount of water released for down-stream usage. The system blends water release rules determined previously using optimization and simulation-based scenario analyses with expert input from an experienced reservoir manager, yielding an intuitive and realistic DSS with which the reservoir manager may easily identify. The DSS also includes a Box-Jenkins time series model that forecasts future inflows. Each month, the system provides the manager with a proposed release schedule, which the manager then uses to explore and evaluate the consequences in terms of the decision criteria, over an extended period of time. The types of information provided to and sought from the manager correspond closely with actual reservoir management practice. An important characteristic of the system is that the manager can quickly explore various different potential release decisions a priori, for a variety of potential inflow scenarios, including predicted inflows for average hydrological years, as well as inflows reflecting extreme events such as drought and flood periods. The manager can compare the results of the release decisions made in the scenario analysis, both with the release strategy proposed by the system and with historical release decisions, thus aiding the manager in establishing effective reservoir management policies in practice. Thus, rather than a mechanical value, our DSS offers the manager a flexible problem analysis with suggested courses of action. We illustrate the system using example sessions with an experienced reservoir manager. While the system is designed specifically to support the management of Lake Kariba, its extension to a more general class of reservoir management problems is straightforward

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Design of optimal reservoir operating rules in large water resources systems combining stochastic programming, fuzzy logic and expert criteria

Given the high degree of development of hydraulic infrastructure in the developed countries, and with the increasing opposition to constructing new facilities in developing countries, the focus of water resource system analysis has turned into defining adequate operation strategies. Better management is necessary to cope with the challenge of supplying increasing demands and conflicts on water allocation while facing climate change impacts. To do so, a large set of mathematical simulation and optimization tools have been developed. However, the real application of these techniques is still limited. One of the main lines of research to fix this issue regards to the involvement of experts' knowledge in the definition of mathematical algorithms. To define operating rules in a way in which system operators could rely, their expert knowledge should be fully accounted and merged with the results from mathematical algorithms. This thesis develops a methodological framework and the required tools to improve the operation of large-scale water resource systems. In such systems, decision-making processes are complex and supported, at least partially, by the expert knowledge of decision-makers. This importance of expert judgment in the operation strategies requires mathematical tools able to embed and combine it with optimization algorithms. The methods and tools developed in this thesis rely on stochastic programming, fuzzy logic and the involvement of system operators during the whole rule-defining process. An extended stochastic programming algorithm, able to be used in large-scale water resource systems including stream-aquifer interactions, has been developed (the CSG-SDDP). The methodological framework proposed uses fuzzy logic to capture the expert knowledge in the definition of optimal operating rules. Once the current decision-making process is fairly reproduced using fuzzy logic and expert knowledge, stochastic programming results are introduced and thus the performance of the rules is improved. The framework proposed in this thesis has been applied to the Jucar river system (Eastern Spain), in which scarce resources are allocated following complex decision-making processes. We present two applications. In the first one, the CSG-SDDP algorithm has been used to define economically-optimal conjunctive use strategies for a joint operation of reservoirs andaquifers. In the second one, we implement a collaborative framework to couple historical records with expert knowledge and criteria to define a decision support system (DSS) for the seasonal operation of the reservoirs of the Jucar River system. The co-developed DSS tool explicitly reproduces the decision-making processes and criteria considered by the system operators. Two fuzzy logic systems have been developed and linked with this purpose, as well as with fuzzy regressions to preview future inflows. The DSS developed was validated against historical records. The developed framework offers managers a simple way to define a priori suitable decisions, as well as to explore the consequences of any of them. The resulting representation has been then combined with the CSG-SDDP algorithm in order to improve the rules following the current decision-making process. Results show that reducing pumping from the Mancha Oriental aquifer would lead to higher systemwide benefits due to increased flows by stream-aquifer interaction. The operating rules developed successfully combined fuzzy logic, expert judgment and stochastic programming, increasing water allocations to the demands by changing the way in which Alarcon, Contreras and Tous are balanced. These rules follow the same decision-making processes currently done in the system, so system operators would feel familiar with them. In addition, they can be contrasted with the current operating rules to determine what operation options can be coherent with the current management and, at the same time, achieve an optimal operationDado el alto número de infraestructuras construidas en los países desarrollados, y con una oposición creciente a la construcción de nuevas infraestructuras en los países en vías de desarrollo, la atención del análisis de sistemas de recursos hídricos ha pasado a la definición de reglas de operación adecuadas. Una gestión más eficiente del recurso hídrico es necesaria para poder afrontar los impactos del cambio climático y de la creciente demanda de agua. Para lograrlo, un amplio abanico de herramientas y modelos matemáticos de optimización se han desarrollado. Sin embargo, su aplicación práctica en la gestión hídrica sigue siendo limitada. Una de las más importantes líneas de investigación para solucionarlo busca la involucración de los expertos en la definición de dichos modelos matemáticos. Para definir reglas de operación en las cuales los gestores confíen, es necesario tener en cuenta su criterio experto y combinarlo con algoritmos de optimización. La presente tesis desarrolla una metodología, y las herramientas necesarias para aplicarla, con el fin de mejorar la operación de sistemas complejos de recursos hídricos. En éstos, los procesos de toma de decisiones son complicados y se sustentan, al menos en parte, en el juicio experto de los gestores. Esta importancia del criterio de experto en las reglas de operación requiere herramientas matemáticas capaces de incorporarlo en su estructura y de unirlo con algoritmos de optimización. Las herramientas y métodos desarrollados se basan en la optimización estocástica, en la lógica difusa y en la involucración de los expertos durante todo el proceso. Un algoritmo estocástico extendido, capaz de ser usado en sistemas complejos con interacciones río-acuífero se ha desarrollado (el CSG-SDDP). La metodología definida usa lógica difusa para capturar el criterio de experto en la definición de reglas óptimas. En primer lugar se reproducen los procesos de toma de decisiones actuales y, tras ello, el algoritmo de optimización estocástica se emplea para mejorar las reglas previamente obtenidas. La metodología propuesta en esta tesis se ha aplicado al sistema Júcar (Este de España), en el que los recursos hídricos son gestionados de acuerdo a complejos procesos de toma de decisiones. La aplicación se ha realizado de dos formas. En la primera, el algoritmo CSG-SDDP se ha utilizado para definir una estrategia óptima para el uso conjunto de embalses y acuíferos. En la segunda, la metodología se ha usado para reproducir las reglas de operación actuales en base a criterio de expertos. La herramienta desarrollada reproduce de forma explícita los procesos de toma de decisiones seguidos por los operadores del sistema. Dos sistemas lógicos difusos se han empleado e interconectado con este fin, así como regresiones difusas para predecir aportaciones. El Sistema de Ayuda a la Decisión (SAD) creado se ha validado comparándolo con los datos históricos. La metodología desarrollada ofrece a los gestores una forma sencilla de definir decisiones a priori adecuadas, así como explorar las consecuencias de una decisión concreta. La representación matemática resultante se ha combinado entonces con el CSG-SDDP para definir reglas óptimas que respetan los procesos actuales. Los resultados obtenidos indican que reducir el bombeo del acuífero de la Mancha Oriental conlleva una mejora en los beneficios del sistema debido al incremento de caudal por relación río-acuífero. Las reglas de operación han sido adecuadamente desarrolladas combinando lógica difusa, juicio experto y optimización estocástica, aumentando los suministros a las demandas mediante modificaciones el balance de Alarcón, Contreras y Tous. Estas reglas siguen los procesos de toma de decisiones actuales en el Júcar, por lo que pueden resultar familiares a los gestores. Además, pueden compararse con las reglas de operación actuales para establecer qué decisiones entreDonat l'alt nombre d'infraestructures construïdes en els països desenrotllats, i amb una oposició creixent a la construcció de noves infraestructures en els països en vies de desenrotllament, l'atenció de l'anàlisi de sistemes de recursos hídrics ha passat a la definició de regles d'operació adequades. Una gestió més eficient del recurs hídric és necessària per a poder afrontar els impactes del canvi climàtic i de la creixent demanda d'aigua. Per a aconseguir-ho, una amplia selecció de ferramentes i models matemàtics d'optimització s'han desenrotllat. No obstant això, la seua aplicació pràctica en la gestió hídrica continua sent limitada. Una de les més importants línies d'investigació per a solucionar-ho busca la col·laboració activa dels experts en la definició dels models matemàtics. Per a definir regles d'operació en les quals els gestors confien, és necessari tindre en compte el seu criteri expert i combinar-ho amb algoritmes d'optimització. La present tesi desenrotlla una metodologia, i les ferramentes necessàries per a aplicar-la, amb la finalitat de millorar l'operació de sistemes complexos de recursos hídrics. En estos, els processos de presa de decisions són complicats i se sustenten, almenys en part, en el juí expert dels gestors. Esta importància del criteri d'expert en les regles d'operació requereix ferramentes matemàtiques capaces d'incorporar-lo en la seua estructura i d'unir-lo amb algoritmes d'optimització. Les ferramentes i mètodes desenrotllats es basen en l'optimització estocàstica, en la lògica difusa i en la col·laboració activa dels experts durant tot el procés. Un algoritme estocàstic avançat, capaç de ser usat en sistemes complexos amb interaccions riu-aqüífer, s'ha desenrotllat (el CSG-SDDP) . La metodologia definida utilitza lògica difusa per a capturar el criteri d'expert en la definició de regles òptimes. En primer lloc es reprodueixen els processos de presa de decisions actuals i, després d'això, l'algoritme d'optimització estocàstica s'empra per a millorar les regles prèviament obtingudes. La metodologia proposada en esta tesi s'ha aplicat al sistema Xúquer (Est d'Espanya), en el que els recursos hídrics són gestionats d'acord amb complexos processos de presa de decisions. L'aplicació s'ha realitzat de dos formes. En la primera, l'algoritme CSG-SDDP s'ha utilitzat per a definir una estratègia òptima per a l'ús conjunt d'embassaments i aqüífers. En la segona, la metodologia s'ha usat per a reproduir les regles d'operació actuals basant-se en criteri d'experts. La ferramenta desenvolupada reprodueix de forma explícita els processos de presa de decisions seguits pels operadors del sistema. Dos sistemes lògics difusos s'han empleat i interconnectat amb este fi, al igual què regressions difuses per preveure cabdals. El Sistema d'Ajuda a la Decisió (SAD) creat s'ha validat comparant-lo amb les dades històriques. La metodologia desenvolupada ofereix als gestors una manera senzilla de definir decisions a priori adequades, així com per explorar les conseqüències d'una decisió concreta. La representació matemàtica resultant s'ha combinat amb el CSG-SDDP per a definir regles òptimes que respecten els processos actuals. Els resultats obtinguts indiquen que reduir el bombament de l'aqüífer de la Mancha Oriental comporta una millora en els beneficis del sistema a causa de l'increment de l'aigua per relació riu-aqüífer. Les regles d'operació han sigut adequadament desenrotllades combinant lògica difusa, juí expert i optimització estocàstica, augmentant els subministres a les demandes per mitjà de modificacions del balanç d'Alarcón, Contreras i Tous. Estes regles segueixen els processos de presa de decisions actuals en el Xúquer, per la qual cosa poden resultar familiars als gestors. A més, poden comparar-se amb les regles d'operació actuals per a establir quines decisions entre les possibles serien coherentsMacián Sorribes, H. (2017). Design of optimal reservoir operating rules in large water resources systems combining stochastic programming, fuzzy logic and expert criteria [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/82554TESI

Socio-hydrological modelling: a review asking “why, what and how?”

Interactions between humans and the environment are occurring on a scale that has never previously been seen; the scale of human interaction with the water cycle, along with the coupling present between social and hydrological systems, means that decisions that impact water also impact people. Models are often used to assist in decision-making regarding hydrological systems, and so in order for effective decisions to be made regarding water resource management, these interactions and feedbacks should be accounted for in models used to analyse systems in which water and humans interact. This paper reviews literature surrounding aspects of socio-hydrological modelling. It begins with background information regarding the current state of socio-hydrology as a discipline, before covering reasons for modelling and potential applications. Some important concepts that underlie socio-hydrological modelling efforts are then discussed, including ways of viewing socio-hydrological systems, space and time in modelling, complexity, data and model conceptualisation. Several modelling approaches are described, the stages in their development detailed and their applicability to socio-hydrological cases discussed. Gaps in research are then highlighted to guide directions for future research. The review of literature suggests that the nature of socio-hydrological study, being interdisciplinary, focusing on complex interactions between human and natural systems, and dealing with long horizons, is such that modelling will always present a challenge; it is, however, the task of the modeller to use the wide range of tools afforded to them to overcome these challenges as much as possible. The focus in socio-hydrology is on understanding the human–water system in a holistic sense, which differs from the problem solving focus of other water management fields, and as such models in socio-hydrology should be developed with a view to gaining new insight into these dynamics. There is an essential choice that socio-hydrological modellers face in deciding between representing individual system processes or viewing the system from a more abstracted level and modelling it as such; using these different approaches has implications for model development, applicability and the insight that they are capable of giving, and so the decision regarding how to model the system requires thorough consideration of, among other things, the nature of understanding that is sought