Causal Inference, Nonlinear Dynamics, and Information Theory Applications in Hydrometeorological Systems

This dissertation consists of six distinct research studies that are broadly classified into two parts. The first part is concerned with the application of emerging data analysis tools rooted in causal inference, nonlinear chaotic dynamical systems, and information theory to detect associations and characterize patterns of interaction in complex hydrometeorological systems. This part is motivated by the rapid accumulation of hydrometeorological data records in the form of in-situ, remotely sensed observations and climatological reconstructions in addition to the significant advancements in data mining tools that facilitate discovery of interaction patterns solely from observational datasets. More specifically, I present four studies that utilize observational datasets to elucidate patterns of interaction and subsequently improve predictive understanding of the underlying processes. First, I evaluate the performance of four causal inference methods in recovering the causal structure underlying a hydrologic conceptual model and utilize causal analysis to formulate hypothesis on the differential impact of environmental variables in regulating evapotranspiration. Second, methods rooted in the theory of chaotic dynamical systems are used to examine the dynamical properties of 400 hydrologic basins across the contiguous United States (CONUS) with the aim of developing a catchment classification framework. Third, I propose an algorithm that utilizes causal inference to extend methods of univariate state space forecasting to account for multivariate predictors. The algorithm is applied for daily streamflow forecasting in nine hydrologic basins across CONUS, and the results are compared to that of deep learning models. Finally, concepts of information theory are used to diagnose the complex, nonlinear, space-time varying relationship between infrared brightness temperature and precipitation across different seasons and spatiotemporal scales. The second part of the dissertation focuses on the use of long historical records of satellite-based precipitation datasets in hydroclimatic research, and it consists of two studies that utilize Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR) dataset. The first study proposes a framework for developing Intensity Duration Frequency (IDF) curves from satellite-based precipitation datasets. The framework accounts for the inherent biases in the estimates of PERSIANN-CDR, and it is used to develop IDF curves over CONUS with evaluation based on in-situ estimates of NOAA Atlas 14. The second study utilizes PERSIANN-CDR dataset over the Nile river basin to constrain future projections of precipitation obtained from climate models. More specifically, a Bayesian Model Averaging (BMA) approach is adopted to constrain future projections of 20 Global Climate Models (GCMs) from phase six of the Coupled Model Intercomparison Project (CMIP6). The results show that annual precipitation is projected to decrease in the upper White Nile basin, whereas projected change in the Blue Nile basin is highly uncertain both in magnitude and sign of change