Análisis del comportamiento hidrodinámico fluvial mediante razonamiento causal y visión artificial

Tesis por compendio de publicaciones[ES]La alteración de los patrones climáticos y meteorológicos así como su repercusión en el ciclo hidrológico, en gran medida atribuible al fenómeno del Calentamiento Global y puesto de manifiesto en las últimas décadas en numerosos estudios, son una realidad global, con dramáticas consecuencias ambientales, sociales y económicas. Eventos extremos tales como precipitaciones, sequias e inundaciones, resultan cada vez menos “anómalos”, y por tanto más recurrentes en el tiempo. Estas nuevas realidades, derivadas de una creciente no-estacionalidad de los procesos hidrológicos e hidráulicos, están provocando una mayor incertidumbre a la hora de modelar y predecir los comportamientos hidrológicos dentro un contexto de gestión sostenible y segura de los recursos hídricos de una cuenca, en el marco del paradigma internacional para la gestión integrada del agua, conocido como “Integrated Water Resources Management” (IWRM). Por otro lado, como sociedad constatamos que las medidas tomadas por parte de instituciones internacionales y gobiernos para revertir los efectos del Calentamiento Global están teniendo un efecto limitado a corto y medio plazo, como consecuencia de la persistencia de los efectos antropogénicos. Dichas medidas mayoritariamente son de adaptación y mitigación frente a estas nuevas realidades hidrológicas. Frente a esta situación, se hace necesario disponer de métodos y técnicas de análisis y observación, eficientes en tiempo y coste, que permitan generar modelos predictivos precisos que agilicen el proceso de toma de decisiones en el ámbito fluvial. Esta Tesis Doctoral, afronta este reto global desde la doble componente hidrológica-geomática y su interdependencia, dado que la cuenca hidrológica se configura como el “medio” donde se pone de manifiesto el “comportamiento” de ésta frente al ciclo hidrológico. En primer lugar se propone una metodología hidrológica de análisis de las series temporales de aportaciones con el fin de profundizar en el conocimiento de las relaciones de dependencia que explique, más en profundidad, su comportamiento. Esto se ha llevado a cabo a través de un enfoque conjunto mediante técnicas tradicionales, basadas en modelos paramétricos (genéricamente expresados como ARIMA; modelos Autorregresivos Integrados de Medias Móviles) y el Razonamiento Causal mediante Inferencia Bayesiana, en consonancia con las nuevas tendencias de la investigación estocástica hidrológica. En segundo lugar, se ha evaluado la idoneidad, entre la hidráulica y la geomática, de nuevos modelos 3D, de coste reducido e incluso bajo coste, continuos, detallados y precisos, generados de modo eficiente (tiempo-coste), y aplicables a la Hidrodinámica y la Hidráulica Fluvial. Estos nuevos modelos 3D (basados en la Visión Artificial y fundamentados en la Geometría Epipolar y la Fotogrametría Digital) son obtenidos mediante la aplicación de técnicas SfM (Structure from Motion). Por otro lado, se proponen dos nuevos métodos para la evaluación de la incertidumbre altimétrica de los modelos digitales de elevación; el primero fundamentado en un enfoque metrológico mediante una evaluación Tipo A de la incertidumbre, y el segundo en los Estimadores Robustos de la centralidad y la dispersión de los errores. En definitiva, se pretende ofrecer un marco metodológico, para incrementar el conocimiento sobre el comportamiento hidrológico-hidráulico de la cuenca hidrográfica, encaminado a una gestión más objetiva, sostenible y segura de los recursos hídricos de una cuenca y de los riesgos naturales de origen fluvial. En esencia, se aspira a conocer para predecir y prevenir

Innovative Risk Assessment Framework for Hydraulic Control of Irrigation Reservoirs´ Breaching

[EN] This research introduces an innovative framework aimed at developing a risk assessment to analyse the breaching hydraulic control of non-impounding reservoirs for irrigation purposes, called irrigation reservoirs (IRs). This approach comprises an analytical method based on several empirical formulas where the one that best fts the diferent geometric characteristics of IR water systems is chosen. Furthermore, a stochastic framework allows for the incorpo ration of the occurrence probability as a tool to characterize the risk analysis of IRs. This occurrence probability has two components: probability based on the bottom elevation of a fnal breach and probability based on the failure mode (piping in this case). In risk assessment terms, the ultimate product comprises the maximum hazard probability maps that allow a signifcant improvement in the representation of the artifcial fooding efect. This research was successfully applied in two dimensions, synthetically and realistically, in the Las Porteras and Macías Picavea IR water systems (Spain). This approach may improve the management of this type of hydraulic infrastructure and its surrounding area by reducing the risk of experiencing negative consequences derived from uncontrolled hydraulic breaching.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE

Qualitative Approach for Assessing Runoff Temporal Dependence Through Geometrical Symmetry

Currently, noticeable changes in traditional hydrological patterns are being observed on the short and medium-term. These modifications are adding a growing variability on water resources behaviour, especially evident in its availability. Consequently, for a better understanding/knowledge of temporal alterations, it is crucial to develop  new analytical strategies which are capable of capturing these modifications on its temporal behaviour. This challenge is here addressed via a purely stochastic methodology on annual runoff time series. This is performed through the propagation of temporal dependence strength over the time, by means of Causality, supported by Causal Reasoning (Bayes’ theorem), via the relative percentage of runoff change that a time-step produces on the following ones. The result is a dependence mitigation graph, whose analysis of its symmetry provides an innovative qualitative approach to assess time-dependency from a dynamic and continuous perspective against the classical, static and punctual result that a correlogram offers. This was evaluated/applied to four Spanish unregulated river sub-basins; firstly on two Douro/Duero River Basin exemplary case studies (the largest river basin at Iberian Peninsula) with a clearly opposite temporal behaviour, and subsequently applied to two watersheds belonging to Jucar River Basin (Iberian Peninsula Mediterranean side), characterised by suffering regular drought conditions. Keywords: Causal reasoning, Theorem of Bayes, Temporal dependence propagation, Runoff time series, Water resources managemen

HydroPredicT_Extreme: A probabilistic method for the prediction of extremal high-flow hydrological events

Disastrous losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements and shift in hydrological trends largely due to Climate Change. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological models’ structure on the magnitude and timing of extreme rainfall-runoff events. This paper is mainly aimed to show the development of a method called “HydroPredicT_Extreme” based on Bayesian Causal Modelling (BCM), a technique within Artificial Intelligence (AI). This method may enhance predictive capacity of extreme rainfall-runoff events. “HydroPredicT_ Extreme” follows an iterative methodology that comprise 2 main stages. First one comprises a mixed graphical/analytical method from Hydrograph. This stage is conditioned by two initial constraints which are, a) pluviometry station is representative of hydrograph downstream flow behaviour; b) there must be independence of events. This first stage comprises sub-phases such as: 1.1. Calculation of Response Time (RT) through a mixed graphical/analytical approach, 1.2 Subtraction of RT from the flow series to remove the Rainfall-Flow delay; 1.3 Calculation base flow rate; 1.4 Subtraction base-flow from flow series to work on absolute inputs. Second man stage is called Bayesian Causal Modelling Translation (BCMT) that comprises the 2.1 Learning, 2.2 Training, 2.3 Simulation through BCM modelling, 2.4 Sensitivity Analysis-Validation. This whole methodology will become a digital application and software that could be extrapolated to several similar case studies. This may be coupled with posterior devices for the prevention of catastrophic flood consequences in the form of MultiHazard-Early Warning System (MH-EWS) or others

DigiTL: diseño de un cronograma digital para la distribución temporal de actividades y tareas formativas en el Máster Universitario en Modelización de Sistemas Hídricos (MODSH)

Memoria ID2022-121 Ayudas de la Universidad de Salamanca para la innovación docente, curso 2022-2023

Assessment of Temporally Conditioned Runoff Fractions in Unregulated Rivers

Increasing nonstationarity increases the uncertainty of hydrological processes. Consequently, intrinsic randomness increases in magnitude and occurrence. In order to generate a reliable and robust predictive model, time series need to be better understood. This study addresses this challenge through the internal causality of annual runoff series. According to new methodological tendencies for hydrological research, complex temporal dependences within time series have been adequately captured through causal reasoning implemented by Bayes’ theorem. Those dependences permit quantifying the relative percentage of annual runoff change attributable to causality. This was later useful for calculating the temporally conditioned/nonconditioned runoff (TCR/TNCR) fractions. Results satisfactorily show the high and low temporally conditioned behavior of Porma-Esla and Adaja subbasin runoff, respectively. This study also provides a new stochastic approach for the return period (RP) assessment. Using TCR and TNCR fractions, the RP for each fraction was calculated and called the temporally conditioned RP (TCRP) and temporally nonconditioned RP (TNCRP), respectively. Results show coherent behavior, and, consequently, the highest RP corresponds to the largest runoff and vice versa

Methodology to Evaluate Aquifers Water Budget Alteration Due to Climate Change Impact on the Snow Fraction

This paper aims to propose a methodology to evaluate and quantify perturbed groundwater budgets considering the projected reduction of Average Snow Fraction of Surface Runoff (ASFSR). Future groundwater budgets are generated considering different CC temporal Scenarios, RCMs, as well as the status of each Groundwater Body (GwB). The proposed methodology is applied to the Central Mountain Range of Iberian Peninsula (Avila Province). Existing studies show a drastic Reduction on Snow Melting (RSM) and on Cumulative Snow Volume (CSV). This leads to a huge reduction of Average Snow Fraction of Surface Runoff (ASFSR) and on groundwater availability calculated through the indicator Perturbed Exploitation Index (PEI). There are important differences depending on the RCM used, on the temporal CC Scenario and on the GwB considered. Main difficulties and challenges comprise the lack of field data and rigorous studies on modelling of groundwater hydrodynamic modelling. Despite of that, research results show a robust and generalized increase in all Exploitation Indexes (EI). EI increase is of 4.17% for IP1 (Short Term) RCP 4.5, 14.94% for IP2 (Medium Term) RCP 4.5, 17.65% for IP3 (Long Term) RCP 4.5. On the other hand, there is an increase of 9.89% for IP1 RCP 8.5, 19.05% for IP2 RCP 8.5 and 35.14% for IP3 RCP 8.5. Thus, there is a generalised and very important decrease of recharge (PARR) of 59.03% for IP1 RCP 4.5, 88.97% for IP2 RCP 4.5, 90.02% for IP3. Likewise, for RCP 8.5, there is a decrease of 72.69% for IP1 RCP 8.5, 88.97% for IP2 RCP 8.5 and 97.90% for IP3

Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff

This paper describes the joint development of two different methods for temporal riverś runoff assessment. This is performed through a hybrid approach by means of Multivariate General Linear Models (MGLM; inspired by MLR as a statistical method), and Causal Reasoning (CR; as non-linear ones). This innovative methodological approach, named Hybrid Causal Multivariate Linear Modelling (H-CMLM), is mainly aimed to empower the analysis of temporal hydrological records behaviour. H-CMLM has been successfully applied to three different Spanish basins (Adaja, Mijares and Porma) which were chosen due to their disparate features. Results were divided in quantitative and qualitative. Numerical results show a very high level of equivalence between the average value of temporal dependence provided by MLM module and the continuous behaviour of temporal dependence computed by CR module and visualized through Dependence Mitigation Graph (DMG). This high coherent outcome from both modules makes the analysis much more robust from a stochastic hydrology point of view. Values for average temporal dependence are very useful for the optimal dimensioning of hydraulic infrastructures like reservoirs. Furthermore, given the annual scale of the analysis, water planning and management of several water uses such as domestic water supply, agriculture, industrial demands, among others, can be highly assisted by this new H_C-MLM method

Analysis of spatio-temporal dependence of in flow time series through Bayesian causal modelling

[EN] This paper aims to assess fully the spatio-temporal dependence dimensions of inflow across two adjacent and parallel basins and among different time steps through Causality. This is addressed from the perspective of Causal Reasoning, supported by Bayesian modelling, under a novel framework named Bayesian Causal Modelling (BCM). This is applied, through a "concept-proof", to the Jucar River Basin (the second largest basin of Eastern Spain, characterized by long and severe drought conditions). In this ¿concept-proof¿ a double goal is evaluated; first dedicated to a lumped analysis of dependence and second a specific one over dry periods focused on time-horizon of the Jucar basin typical drought (3 years). These challenges comprise the development of two fully connected Bayesian Networks (BNs), one for each challenge populated/trained from historical-inflow records. BNs were designed at a season-scale and consequently, time was upscaled and grouped into Irrigation and Non-Irrigation periods, according to Jucar River Basin Authority operational practices. Results achieved showed that BCM framework satisfactorily captured the spatio-temporal dependencies of systems. Furthermore, BCM is able to answer to some key questions over interdependencies between adjacent and parallel subbasins. Those questions may comprise, the amount of spatial dependences among time series, the temporarily conditionality among subbasins and the spatio-temporal dependence among basins. This provides a relevant insight on the intrinsic spatio-temporal dependence structure of inflow time series in complex basins systems. 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