Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach

Abstract

Exploring water fluxes between hydrological model (HM) components is essential to assess and improve model realism. Many classical metrics for HM diagnosis rely solely on streamflow and hence provide limited insights into model performance across processes. This study applies an information theory measure known as "transfer entropy" (TE) to systematically quantify the transfer of information among major HM components. To test and demonstrate the benefits of TE, we use the Framework for Understanding Structural Errors (FUSE) model to mimic and compare four commonly used HM structures, VIC, PRMS, SACRAMENTO, and TOPMODEL, across 671 catchments spanning a variety of hydrologic regimes in the conterminous United States. We explore connections between HM components and catchment landscape characteristics (e.g., climate, topography, soil, and vegetation) and characterize their nonlinear associations using distance correlation and Spearman correlation coefficients. Our results indicate that while the information transferred from precipitation to runoff is similar across model structures (likely as a result of calibration), the information transferred among other components can vary significantly from a FUSE structure to another. We find that aridity, precipitation duration and frequency, snow fraction, mean elevation, forest area, and leaf area index are often significantly associated with TE between the main HM components. We propose that the presence of meaningful nonlinear associations can be used to diagnose process representation in HMs. Our results highlight the necessity to enhance the conventional streamflow-only calibration approach for a more realistic representation of water dynamics in the models.

The sensitivity of simulated streamflow to individual hydrologic processes across North America

“…The predictor with the largest influence is the fraction of precipitation as snow f S , which is used as a predictor in eight of the nine relationships. This is in close agreement with the findings by Konapala et al 64 , who also identified the fraction of snow as a key characteristic to explain runoff signals in their study based on an information theory approach. The four processes that benefit from adding a second predictor are Quickflow, Convolution of Surface Runoff, Rain-Snow Partitioning and Precipitation Correction.…”

Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models

“…The hydrological models and mechanisms for hypothesis testing are generated using the FUSE, a multihypothesis hydrological modeling system designed to facilitate work on model representation and improvement (e.g., Clark et al, 2008;2011a;Konapala et al, 2020). The FUSE mechanisms are represented by components of existing models, namely, PRMS, SACRAMENTO, TOPMODEL, and ARNO/VIC.…”

Cyclocopula Technique to Study The Relationship Between Two Cyclostationary Time Series With Fractional Brownian Motion Errors

“…The choice of the better model depends on the dependency structure of obligators and is crucial part of the modeling. The applied methods in previous studies include Pearson's correlation coefficient [1][2][3][4][5][6], Spearman's correlation coefficient [7][8][9][10], Kendall's correlation coefficient [11][12][13][14], Sen's slope [15][16][17], cross-correlation function [18][19][20] and copula [21][22][23][24][25][26]. When we face with the relationship of two stationary time series, crosscorrelation function and copula are suggested.…”