Tesis por compendio / [ES] La mayoría de los estudios científicos pronostican un incremento en la frecuencia y magnitud de los episodios de precipitaciones extremas como consecuencia de los efectos del cambio climático. Además, se espera que en un plazo de 50 años el 80% de la población mundial viva en zonas propensas a inundaciones. Este incremento en la peligrosidad, vulnerabilidad y en la exposición al peligro de lluvias intensas supone un aumento significativo en el riesgo de inundaciones, ya de por si elevado, que manifiesta la urgente necesidad de tomar medidas encaminadas a reducir la vulnerabilidad y desarrollar metodologías capaces de estimar con la mayor precisión posible la magnitud y la probabilidad de ocurrencia de estos posibles fenómenos extremos. En esta última dirección va dirigida la presente tesis doctoral, presentando una nueva metodología basada en el uso de generadores meteorológicos estocásticos para la estimación de la frecuencia de avenidas extremas tanto en escenarios de clima actual como de cambio climático. Más allá del paradigma de la tormenta de diseño y de los estudios tradicionales de análisis de frecuencia de inundaciones, la metodología propuesta en esta tesis se basa en la simulación sintética continua: generador meteorológico estocástico + modelo hidrológico espacialmente distribuido. El uso de generadores meteorológicos estocásticos para el análisis de frecuencia de inundaciones es una práctica cada vez más común dentro de la comunidad hidrológica. Sin embargo, es necesario disponer de observaciones largas y completas para obtener estimaciones de cuantiles confiables para altos períodos de retorno. La novedad que introduce la metodología propuesta se basa en la integración de estudios regionales de precipitación máxima en la implementación del generador meteorológico, lo que reduce considerablemente la incertidumbre en las estimaciones de cuantiles (especialmente aquellos asociados a eventos de baja frecuencia) debida a los usualmente cortos y escasos registros hidrometeorológicos de los que se dispone hasta la fecha. Esta tesis se presenta como un compendio de cinco publicaciones: tres de ellas ya publicadas y dos en proceso de revisión en revistas indexadas en el Journal Citation Report. Estos documentos narran la progresión de la metodología a lo largo de diversas etapas hasta llegar al enfoque final. Inicialmente concebida para el clima actual a escala diaria, la metodología fue posteriormente adaptada a escala subdiaria y finalmente desarrollada para su aplicación en escenarios de clima futuro. A lo largo de este proceso, se abordaron estudios de incertidumbre asociados a la cantidad de información que involucran tanto las estimaciones de cuantiles de precipitación como de inundación. Las metodologías se han implementado en dos casos de estudio: Rambla de la Viuda (Castellón) y; la cuenca del río Segura, cuyos resultados han evidenciado la solidez y eficacia de las metodologías. En el ámbito de la modelización meteorológica, los resultados han sido consistentes y satisfactorios, demostrando la capacidad de la metodología para representar con precisión las complejidades de los patrones climáticos. Asimismo, en el ámbito hidrológico, la metodología ha exhibido una eficaz capacidad para representar y simular los procesos relacionados con el ciclo del agua, ofreciendo resultados coherentes y satisfactorios en la estimación de caudales y eventos de inundación tanto en clima actual como en clima futuro. Esta consistencia en la robustez de la metodología, tanto en la modelización meteorológica como hidrológica, respalda su aplicabilidad y confiabilidad en entornos y condiciones climáticas diversas. / [CA] La majoria dels estudis científics pronostiquen un increment de la freqüència i la magnitud dels episodis de precipitacions extremes a conseqüència dels efectes del canvi climàtic. A més, s'espera que en un termini de 50 anys el 80% de la població mundial habite en zones propenses a inundacions. Aquest acreixement en la perillositat, vulnerabilitat i exposició al perill de pluges intenses suposa un augment significatiu del risc d'inundacions, ja de per si elevat, que manifesta la urgent necessitat de prendre mesures encaminades a reduir la vulnerabilitat i desenvolupar metodologies que permeten estimar amb la major precisió possible la magnitud i la probabilitat d'ocurrència d'aquests possibles fenòmens extrems. En aquesta última direcció va dirigida la present tesi doctoral, que presenta una nova metodologia basada en l'ús de generadors meteorològics estocàstics per a l'estimació de la freqüència d'avingudes extremes, tant en escenaris de clima actual com de canvi climàtic. Més enllà del paradigma de la tempesta de disseny i dels estudis tradicionals d'anàlisis de freqüència d'inundacions, la metodologia proposada en aquesta tesi es basa en la simulació sintètica contínua: generador meteorològic estocàstic + model hidrològic espacialment distribuït. L'ús de generadors meteorològics estocàstics per a l'anàlisi de freqüència d'inundacions és una pràctica cada vegada més comuna dins de la comunitat hidrològica. No obstant això, és necessari disposar d'observacions llargues i completes per a obtindre estimacions de quantils de confiança per a alts períodes de retorn. La novetat que introdueix la metodologia proposada es basa en la integració d'estudis regionals de precipitació màxima en la implementació del generador meteorològic, la qual cosa redueix considerablement la incertesa en les estimacions de quantils (especialment d'aquells associats a esdeveniments de baixa freqüència) a causa dels usualment curts i escassos registres hidrometeorològics dels quals es disposa fins a la data. Auqesta tesi es presenta com un compendi de cinc publicacions: tres d'elles en ja publicades i dos en procés de revisió en revistes indexades en el Journal Citation Report. Aquests documents narren la progressió de la metodologia al llarg de diverses etapes fins arribar a l'enfocament final. Inicialment concebuda per al clima actual a escala diària, la metodologia va ser posteriorment adaptada a escala subdiària i finalment desenvolupada per a la seua aplicació en escenaris de clima futur. Al llarg d'aquest procés, es van abordar estudis d'incertesa associats a la quantitat d'informació que involucren tant les estimacions de quantils de precipitació com d'inundació. Les metodologies s'han implementat en dos casos d'estudi: Rambla de la Vídua (Castelló) i; la conca del riu Segura, els resultats del qual han evidenciat la solidesa i eficàcia de les metodologies. En l'àmbit de la modelització meteorològica, els resultats han estat consistents i satisfactoris, ja que han demostrat la capacitat de la metodologia per a representar amb precisió les complexitats dels patrons climàtics. Així mateix, en l'àmbit hidrològic, la metodologia ha exhibit una eficaç capacitat per a representar i simular els processos relacionats amb el cicle de l'aigua, i ens ha oferit resultats coherents i satisfactoris en l'estimació de cabals i esdeveniments d'inundació tant en clima actual com en clima futur. Aquesta consistència en la robustesa de la metodologia, tant en la modelització meteorològica com en la hidrològica, recolza la seua aplicabilitat i confiabilitat en entorns i condicions climàtiques diverses. / [EN] Most scientific studies predict an increase in the frequency and magnitude of extreme precipitation events as a consequence of climate change effects. Furthermore, it is expected that within 50 years, 80% of the global population will reside in flood-prone areas. This heightened risk, vulnerability, and exposure to intense rainfall hazards signify a significant rise in the flood risk, already elevated. It underscores the urgent need to implement measures to reduce vulnerability and develop methodologies capable of accurately estimating the magnitude and probability of occurrence of these potential extreme events. This doctoral thesis is directed towards this objective, presenting a new methodology based on the use of stochastic weather generators for estimating the frequency of extreme floods in both current and climate change scenarios. Beyond the Design Storm paradigm and traditional Flood Frequency Analysis studies, the methodology proposed in this thesis relies on continuous synthetic simulation: stochastic weather generator + spatially distributed hydrological model. The use of stochastic weather generators for Flood Frequency Analysis is becoming increasingly common within the hydrological community. However, reliable quantile estimates for high return periods require long and complete observations. The innovation introduced by the proposed methodology lies in the integration of regional studies of maximum precipitation into the implementation of the weather generator, significantly reducing uncertainty in quantile estimates (especially those associated with low-frequency events) due to the typically short and limited hydrometeorological records available to date. This thesis is presented as a compilation of five publications: three already published and two under review in journals indexed in the Journal Citation Report. These documents narrate the progression of the methodology through various stages to its final approach. Initially conceived for the current climate at a daily scale, the methodology was later adapted to a subdaily scale and ultimately developed for application in future climate scenarios. Throughout this process, uncertainty studies were addressed concerning the amount of information involving both precipitation and flood quantile estimates. The methodologies have been implemented in two case studies: Rambla de la Viuda (Castellón) and the Segura River basin, with results demonstrating the robustness and effectiveness of the methodologies. In the field of meteorological modeling, the results have been consistent and satisfactory, showcasing the methodology's ability to accurately represent the complexities of climate patterns. Likewise, in the hydrological domain, the methodology has exhibited effective capabilities in representing and simulating processes related to the water cycle, offering coherent and satisfactory results in the estimation of flows and flood events in both current and future climates. This consistency in the robustness of the methodology, both in meteorological and hydrological modeling, supports its applicability and reliability in diverse environmental and climatic conditions. / This work was supported by the Spanish Ministry of Science and Innovation through the research projects TETISCHANGE (RTI2018-093717-B-100) and TETISPREDICT (PID2022-141631OB-I00). Funding for the Open Access charge has been provided by Universitat Politècnica de València / Beneyto Ibáñez, C. (2024). On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/205179 / Compendio

Reliable estimates of design flood quantiles are often necessary at sparsely gauged/ungauged target locations in river basins for various applications in water resources engineering. Development of effective methods for use in this task has been a long-standing challenge in hydrology for over five decades.. Hydrologists often consider various regional flood frequency analysis (RFFA) approaches that involve (i) use of regionalization approach to delineate a homogeneous group of watersheds resembling watershed of the target location, and (ii) use of a regional frequency analysis (RFA) approach to transfer peak flow related information from gauged watersheds in the group to the target location, and considering the information as the basis to estimate flood quantile(s) for the target site. The work presented in the thesis is motivated to address various shortcomings/issues associated with widely used regionalization and RFA approaches. Regionalization approaches often determine regions by grouping data points in multidimensional space of attributes depicting watershed’s hydrology, climatology, topography, land-use/land-cover and soils. There are no universally established procedures to identify appropriate attributes, and modelers use subjective procedures to choose a set of attributes that is considered common for the entire study area. This practice may not be meaningful, as different sets of attributes could influence extreme flow generation mechanism in watersheds located in different parts of the study area. Another issue is that practitioners usually give equal importance (weight) to all the attributes in regionalization, though some attributes could be more important than others in influencing peak flows. To address this issue, a two-stage clustering approach is developed in the thesis. It facilitates identification of appropriate attributes and their associated weights for use in regionalization of watersheds in the context of flood frequency analysis. Effectiveness of the approach is demonstrated through a case study on Indiana watersheds. Conventional regionalization approaches could prove effective for delineating regions when data points (depicting watersheds) in watershed related attribute space can be segregated into disjoint groups using straight lines or linear planes. They prove ineffective when (i) data points are not linearly separable, (ii) the number of attributes and watersheds is large, (iii) there are outliers in the attribute space, and (iv) most watersheds resemble each other in terms of their attributes. In real world scenario, most watersheds resemble each other, and regions may not always be segregated using straight lines or linear planes, and dealing with outliers and high-dimensional data is inevitable in regionalization. To address this, a fuzzy support vector clustering approach is proposed in the thesis and its effectiveness over commonly used region-of-influence approach, and different cluster analysis based regionalization methods is demonstrated through a case study on Indiana watersheds. For the purpose of regional frequency analysis (RFA), index-flood approach is widely used over the past five decades. Conventional index-flood (CIF) approach assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real world scenario, this assumption may not be valid even if a region is statistically homogeneous. Logarithmic index-flood (LIF) and population index-flood (PIF) methodologies were proposed to address the problem, but even those methodologies make unrealistic assumptions. PIF method assumes that the ratio of scale to location parameters is a constant for all the sites in a region. On the other hand, LIF method assumes that appropriate frequency distribution to fit peak flows could be found in log-space, but in reality the distribution of peak flows in log space may not be closer to any of the known theoretical distributions. To address this issue, a new mathematical approach to RFA is proposed in L-moment and LH-moment frameworks that can overcome shortcomings of the CIF approach and its related LIF and PIF methods that make various assumptions but cannot ensure their validity in RFA. For use with the proposed approach, transformation mechanisms are proposed for five commonly used three-parameter frequency distributions (GLO, GEV, GPA, GNO and PE3) to map the random variable being analyzed from the original space to a dimensionless space where distribution of the random variable does not change, and deviations of regional estimates of all the distribution’s parameters (location, scale, shape) with respect to their population values as well as at-site estimates are minimal. The proposed approach ensures validity of all the assumptions of CIF approach in the dimensionless space, and this makes it perform better than CIF approach and related LIF and PIF methods. Monte-Carlo simulation experiments revealed that the proposed approach is effective even when the form of regional frequency distribution is mis-specified. Case study on watersheds in conterminous United States indicated that the proposed approach outperforms methods based on index-flood approach in real world scenario. In recent decades, fuzzy clustering approach gained recognition for regionalization of watersheds, as it can account for partial resemblance of several watersheds in watershed related attribute space. In working with this approach, formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix. But, currently there are no effective procedures available for discerning the information. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies, but it results in under-prediction of quantiles. To address this, a mathematical approach is proposed in the thesis that allows discerning information from fuzzy-membership matrix derived using fuzzy clustering approach for effective quantile estimation. Effectiveness of the approach in estimating flood quantiles relative to DFA and TS was demonstrated through Monte-Carlo simulation experiments and case study on mid-Atlantic water resources region, USA. Another issue with index flood approach and its related RFA methodologies is that they assume linear relationship between each of the statistical raw moments (SMs) of peak flows and watershed related attributes in a region. Those relationships form the basis to arrive at estimates of SMs for the target ungauged/sparsely gauged site, which are then utilized to estimate parameters of flood frequency distribution and quantiles corresponding to target return periods. In reality, non-linear relationships could exist between SMs and watershed related attributes. To address this, simple-scaling and multi-scaling methodologies have been proposed in literature, which assume that scaling (power law) relationship exists between each of the SMs of peak flows at sites in a region and drainage areas of watersheds corresponding to those sites. In real world scenario, drainage area alone may not completely describe watershed’s flood response. Therefore flood quantile estimates based on the scaling relationships can have large errors. To address this, a recursive multi-scaling (RMS) approach is proposed that facilitates construction of scaling (power law) relationship between each of the SMs of peak flows and a set of site’s region-specific watershed related attributes chosen/identified in a recursive manner. The approach is shown to outperform index-flood based region-of-influence approach, simple-and multi-scaling approaches, and a multiple linear regression method through leave-one-out cross validation experiment on watersheds in and around Indiana State, USA. The conventional approaches to flood frequency analysis (FFA) are based on the assumption that peak flows at the target site represent a sample of independent and identically distributed realization drawn from a stationary homogeneous stochastic process. This assumption is not valid when flows are affected by changes in climate and/or land use/land cover, and regulation of rivers through dams, reservoirs and other artificial diversions/storages. In situations where evidence of non-stationarity in peak flows is strong, it is not appropriate to use quantile estimates obtained based on the conventional FFA approaches for hydrologic designs and other applications. Downscaling is one of the options to arrive at future projections of flows at target sites in a river basin for use in FFA. Conventional downscaling methods attempt to downscale General Circulation Model (GCM) simulated climate variables to streamflow at target sites. In real world scenario, correlation structure exists between records of streamflow at sites in a study area. An effective downscaling model must be parsimonious, and it should ensure preservation of the correlation structure in downscaled flows to a reasonable extent, though exact reproduction/mimicking of the structure may not be necessary in a climate change (non-stationary) scenario. A few recent studies attempted to address this issue based on the assumption of spatiotemporal covariance stationarity. However, there is dearth of meaningful efforts especially for multisite downscaling of flows. To address this, multivariate support vector regression (MSVR) based methodology is proposed to arrive at flood return levels (quantile estimates) for target locations in a river basin corresponding to different return periods in a climate change scenario. The approach involves (i) use of MSVR relationships to downscale GCM simulated large scale atmospheric variables (LSAVs) to monthly time series of streamflow at multiple locations in a river basin, (ii) disaggregation of the downscaled streamflows corresponding to each site from monthly to daily time scale using k-nearest neighbor disaggregation methodology, (iii) fitting time varying generalized extreme value (GEV) distribution to annual maximum flows extracted from the daily streamflows and estimating flood return levels for different target locations in the river basin corresponding to different return periods.