Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.We thank Jorge Perez for the ESTELA code. A.R., J.A.A.A., and F.J.M. acknowledge the support of the Spanish ‘‘Ministerio de Economia y Competitividad’’ under grant BIA2014-59643-R. P.C. acknowledges the support of the Spanish ‘‘Ministerio de Economia y Competitividad’’ under grant BIA2015-70644-R. J.A.A.A. is indebted to the MEC (Ministerio de Educacion, Cultura y Deporte, Spain) for the funding provided in the FPU (Formacion del ProfesoradoUniversitario) studentship (BOE-A-2013-12235). This material is based upon work supported by the U.S. Geological Survey under grant/cooperative agreement G15AC00426. P.R. acknowledges the support of the National Oceanic and Atmospheric Administration Climate Program Office via award NA15OAR4310145. Support was provided from the US DOD Strategic Environmental Research and Development Program (SERDP Project RC-2644) through the NOAA National Centers for Environmental Information (NCEI). Atmospheric data from CFSR are available online at https://climatedataguide.ucar.edu/climatedata/climate-forecast-system-reanalysis-cfsr. Marine data from global reanalysis are lodge with the IHData center from IHCantabria and are available for research purposes upon request (contact: [email protected])

Copulas in Hilbert spaces

In this article, the concept of copulas is generalised to infinite dimensional Hilbert spaces. We show one direction of Sklar's theorem and explain that the other direction fails in infinite dimensional Hilbert spaces. We derive a necessary and sufficient condition which allows to state this direction of Sklar's theorem in Hilbert spaces. We consider copulas with densities and specifically construct copulas in a Hilbert space by a family of pairwise copulas with densities

Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development

The current practice of drought declaration (US Drought Monitor) provides a hard classification of droughts using various hydrologic variables. However, this method does not yield model uncertainty, and is very limited for forecasting upcoming droughts. The primary goal of this thesis is to develop and implement methods that incorporate uncertainty estimation into drought characterization, thereby enabling more informed and better decision making by water users and managers. Probabilistic models using hydrologic variables are developed, yielding new insights into drought characterization enabling fundamental applications in droughts

A CFD-informed model for subchannel resolution crud prediction

A physics-directed, statistically based, surrogate model of the small scale flow fea- tures that impact Chalk River unidentified deposit (crud) growth is presented in this work. The objective of the surrogate is to provide additional details of the rod surface temperature, heat flux, and near-wall turbulent kinetic energy fields which cannot be explicitly captured by a subchannel code. Operating as a mapping from the high fidelity computational fluid dynamics (CFD) data to the low fidelity subchannel grid (hi2lo), the model provides CFD-informed bound- ary conditions to the crud model executed on the subchannel pin surface mesh. The surface temperature, heat flux, and turbulent kinetic energy, henceforth referred to as the fields of interest (FOI), govern the growth rate of crud on the surface of the rod and the precipitation of boron in the porous crud layer. Therefore the model predicts the behavior of the FOIs as a function of position in the core and local thermal-hydraulic (TH) conditions. The subchannel code produces an estimate for all crud-relevant TH quantities at a coarse spatial resolution everywhere in the core and executes substantially faster than CFD. In the hi2lo approach, the solution provided by the subchannel code is augmented by a predicted stochastic component of the FOI informed by CFD results to provide a more detailed description of the target FOIs than subchannel can provide alone. To this end, a novel method based on the marriage of copula and gradient boosting techniques is proposed. This methodology forgoes a spatial interpolation procedure for a statistically driven approach, which predicts the fractional area of a rod’s surface in excess of some critical temperature but not precisely where such maxima occur on the rod surface. The resultant model retains the ability to account for the presence of hot and cold spots on the rod surface induced by turbulent flow downstream of spacer grids when producing crud estimates. Sklar’s theorem is leveraged to decompose multivariate probability densities of the FOI into independent copula and marginal models. The free parameters within the copula model are predicted using a combination of supervised regression and classification machine learning techniques with training data sets supplied by a suite of precomputed CFD results spanning a typical pressurized water reactor TH envelope. Results show that compared to the subchannel standalone case, the hi2lo method more accurately preserves the influence of spacer grids on the crud growth rate. Or more precisely, the hi2lo method recovers key statistical properties of the FOI which impact crud growth. Compared to gold standard high fidelity CFD/crud coupled results in a single assembly test case, the hi2lo model produced a relative total crud mass difference of -8.9% compared to the standalone subchannel relative crud mass difference of 192.1%.Mechanical Engineerin

Forecasting seasonal rainfall with copula modelling approach for agricultural stations in Papua New Guinea

Developing innovative forecasting tools is important to address issues related to climate change, agriculture, and economy of small Pacific Island nations. Papua New Guinea, PNG is a developing nation that is vulnerable to the imminent threats of climate change and influences agricultural sector that supports a majority of its citizens. Accurate modeling and forecasting methods for both monthly and seasonal rainfall (that influences agricultural and other human activities) by employing large-scale climate mode indices (linked to rainfall events) are significant predictive tools for developing climate resilience and productivity in agricultural activities. Copula statistical models, developed in this Master’s study, are considered as viable alternative tools to fulfill this objective. This Masters by Research Thesis utilizes the D-vine copula-based quantile regression methods that are developed to create a model between statistically significant lagged relationships and joint influences of large-scale climate mode indices such as the El-Niño Southern Oscillation (ENSO) and Indian Ocean Dipole- on seasonal rainfall data across four major agricultural-based weather stations. Copula techniques allow the respective model to fully capture the dependence structure between input(s) and the target variable regardless of the marginal distribution of each variable. The D-vine copula-based quantile approach, used in this study, through Akaike information criterion (AIC)-corrected conditional log-likelihood (cllAIC) can also enable researchers to identify the most influent predictor variables for seasonal rainfall forecasting. To forecast the monthly and the respective seasonal rainfall for PNG, an agricultural-reliant nation, the statistically significant lagged correlations between ENSO indicators (e.g., SOI, Nino3.0, etc.) and the IOD indicator (i.e., DMI) with a three-monthly total rainfall were established for up to 7 months ahead time. For example, in a 'lead-0' timescale case study for seasonal rainfall forecasting, this study has utilized the January to March average SOI (as a model input) relative to the April to June total rainfall (as the target variable) deduced by the Kendall rank correlation coefficients established between the input and the target variable. In terms of the results of this study, a correlation analysis performed between the most optimal lead times considering climate mode indices and the three-monthly total rainfall were found to be consistent with the most influent predictor variables identified from the D-vine copula-based quantile model (as a basis to generate bivariate models that captured ENSO impacts on rainfall). To further explore any improvements in rainfall forecast model accuracy, particularly, the extreme rainfall events, the study has also considered the impact of Indian Ocean Dipole (IOD) index by embedding the DMI into the bivariate model to finally construct a trivariate forecast models that accounts for compound effects of ENSO and IOD on extreme rainfall events. To ascertain the versatility of the proposed copula-based forecast models as a major contribution of this study, a number of statistical score metrics based on the Willmott's Index (d), Nash–Sutcliffe Efficiency (ENS), Legates-McCabe’s Index (L), root-mean-square-error (RMSE), and mean absolute error (MAE), including the Relative Root Mean Square Error (RRMSE) and Mean Absolute Percentage Error (MAPE) are computed from forecasted and observed rainfall data in the testing phase. It was evident that the station Aiyura attained the best result for both the bivariate and the trivariate model, exhibiting r = 0.63, RMSE = 105.99, MAE = 89.75, ENS = 0.63, d = 0.38, L=0.20 with, the RRMSE =15.39% for the bivariate study, whereas the trivariate model evaluations generated a score metric of 0.68, 0.42, 0.28 and 14.84%, respectively. In summary, the copula statistical modelling approaches contributed by this study, can be enabling mechanisms for climate change resilience, measuring and implementing risk management strategies. These predictive tools can have significant implications for applications in many socioeconomic sectors such as water resources management, better farming practices for crop health, and other agricultural management not only in the present study region but also in the other agricultural-reliant nations where rainfall prediction is often challenging task

Integrated High-Resolution Modeling for Operational Hydrologic Forecasting

Current advances in Earth-sensing technologies, physically-based modeling, and computational processing, offer the promise of a major revolution in hydrologic forecasting—with profound implications for the management of water resources and protection from related disasters. However, access to the necessary capabilities for managing information from heterogeneous sources, and for its deployment in robust-enough modeling engines, remains the province of large governmental agencies. Moreover, even within this type of centralized operations, success is still challenged by the sheer computational complexity associated with overcoming uncertainty in the estimation of parameters and initial conditions in large-scale or high-resolution models. In this dissertation we seek to facilitate the access to hydrometeorological data products from various U.S. agencies and to advanced watershed modeling tools through the implementation of a lightweight GIS-based software package. Accessible data products currently include gauge, radar, and satellite precipitation; stream discharge; distributed soil moisture and snow cover; and multi-resolution weather forecasts. Additionally, we introduce a suite of open-source methods aimed at the efficient parameterization and initialization of complex geophysical models in contexts of high uncertainty, scarce information, and limited computational resources. The developed products in this suite include: 1) model calibration based on state of the art ensemble evolutionary Pareto optimization, 2) automatic parameter estimation boosted through the incorporation of expert criteria, 3) data assimilation that hybridizes particle smoothing and variational strategies, 4) model state compression by means of optimized clustering, 5) high-dimensional stochastic approximation of watershed conditions through a novel lightweight Gaussian graphical model, and 6) simultaneous estimation of model parameters and states for hydrologic forecasting applications. Each of these methods was tested using established distributed physically-based hydrologic modeling engines (VIC and the DHSVM) that were applied to watersheds in the U.S. of different sizes—from a small highly-instrumented catchment in Pennsylvania, to the basin of the Blue River in Oklahoma. A series of experiments was able to demonstrate statistically-significant improvements in the predictive accuracy of the proposed methods in contrast with traditional approaches. Taken together, these accessible and efficient tools can therefore be integrated within various model-based workflows for complex operational applications in water resources and beyond