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dc.contributor.advisorValdés, Juan B.
dc.contributor.advisorFerré, Ty
dc.contributor.authorValdes-Pineda, Rodrigo M.
dc.creatorValdes-Pineda, Rodrigo M.
dc.date.accessioned2021-02-20T02:32:04Z
dc.date.available2021-02-20T02:32:04Z
dc.date.issued2020
dc.identifier.citationValdes-Pineda, Rodrigo M. (2020). Operational Short-Range to Long Range (SR2LR) Streamflow Forecasting for Poorly Gauged Basins: The Unexplored Dimension of Variational Ensemble Forecasting, the Spatio-Temporal Structure of Modeling Paradigms, and the Role of Machine Learning Strategies to Improve Hydrological Hypotheses (Doctoral dissertation, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/656826
dc.description.abstractThe Prediction in Ungauged Basins (PUB) Initiative was created to advance scientific understanding and estimation of hydrological processes, as well as associated uncertainties, to improve prediction capabilities in basins which are poorly gauged (Sivapalan et al., 2003; Blöschl et al. 2013; Blöschl, 2016; Blöschl et al., 2019). The main objectives defined by the PUB Initiative were to (1) improve the ability of existing hydrologic models to predict with reduced uncertainty, and to (2) develop new and innovative models representing the space–time variability of hydrological processes (Hrachowitz et al., 2013). Despite the International Association of Hydrological Sciences (IAHS) dedicated an entire decade (2003-2012) to advance the problem of Prediction in Ungauged Basins (Hrachowitz et al. 2013), the central goal remains largely a challenge (Kratzert et al., 2020).This dissertation discusses the main steps and decisions required to implement, calibrate, and validate an operational (real-time) Hydrological Forecasting System (HFS), for short-range to long-range (SR2LR) daily streamflow forecasting. The HFS was implemented under an operational context, and experimentally evaluated in the “poorly-gauged” Upper Zambezi River Basin (UZRB) and its “ungauged” sub-basins. The state of art focuses on describing several hydrological modelling strategies (HMS), discussing the way hydrological ensembles have been traditionally performed, and how meteorological and hydrological uncertainty have been quantified. Additionally, a novel Variational Ensemble Forecasting (VEF) approach was applied and evaluated assuming that any combination of multiple inputs, models, and optimal parameters sets, is a practical hydrological ensemble that can be used to reproduce daily streamflow forecasts with reduced total uncertainty. The VEF approach implemented allowed for increasing the number of hydrological ensembles (outputs) from all possible combinations of multiple satellite products (or multiple climate models), hydrologic models, and optimal parameter sets. The performance of VEF was compared and evaluated with classical approaches used to develop hydrological ensembles (input-model-output). To complement the application of the VEF approach, three hydrologic processing hypotheses (HPH) to quantify the hydrological uncertainty propagated from the components of a modelling chain were used: (1) Hydrological Pre-Processing (HPR); (2) Hydrological Processing (HP), and (3) Hydrological Post-Processing (HPP). These HPH’s were evaluated with practical examples in the UZRB and its sub-basins, proving to be a more efficient and systematic way to quantify and reduce the uncertainty propagated from an operational VEF implementation. To inform the development of reliable operational hydrologic forecasting products, the HPR hypotheses are evaluated through the analysis of the spatio-temporal structure of meteorological uncertainty. This analysis allowed determining what factors dominate the propagation of meteorological uncertainty for the operational implementation of distributed hydrologic modeling strategies i.e. the spatial resolution of a meteorological input, the leading time of the forecasts associated to it, and the size of the basin under evaluation. The role of machine learning (ML) approaches for daily streamflow forecasting was also evaluated by coupling VEF with ML techniques (VEF-ML approach). In doing so, several hydrologic learning strategies (inference versus pattern-based) were compared and evaluated to improve the HFS performance through hydrologic post-processing hypotheses (see i.e. Nearing et al., 2020 a, b; Gauch et al., 2020). Lastly, we will discuss how the properties of hydrologic forecasts can be improved by applying Hydrologic Forecast Skill Analysis (FCA) to answer the following question: How to perform a Skill Analysis of Seasonal Hydrologic Streamflow Forecasts? The main contributions and results of this dissertation highlight several opportunities and challenges for future research aimed to advance the main goal and the corresponding objectives of the PUB initiative.
dc.language.isoen
dc.publisherThe University of Arizona.
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectHydrologic Forecasting System
dc.subjectReal-Time Streamflow Forecasting
dc.subjectSatellite Precipitation Products
dc.subjectUngauged Basins
dc.subjectVariational Ensemble Forecasting
dc.titleOperational Short-Range to Long Range (SR2LR) Streamflow Forecasting for Poorly Gauged Basins: The Unexplored Dimension of Variational Ensemble Forecasting, the Spatio-Temporal Structure of Modeling Paradigms, and the Role of Machine Learning Strategies to Improve Hydrological Hypotheses
dc.typetext
dc.typeElectronic Dissertation
thesis.degree.grantorUniversity of Arizona
thesis.degree.leveldoctoral
dc.contributor.committeememberTroch, Peter
dc.contributor.committeememberTrouet, Valerie
thesis.degree.disciplineGraduate College
thesis.degree.disciplineHydrology
thesis.degree.namePh.D.
refterms.dateFOA2021-02-20T02:32:04Z


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