Predictability of river flow and suspended sediment transport in the Mississippi River basin: a non‐linear deterministic approach

Abstract

As the Mississippi River plays a major role in fulfilling various water demands in North America, accurate prediction of river flow and sediment transport in the basin is crucial for undertaking both short-term emergency measures and long-term management efforts. To this effect, the present study investigates the predictability of river flow and suspended sediment transport in the basin. As most of the existing approaches that link water discharge, suspended sediment concentration and suspended sediment load possess certain limitations (absence of consensus on linkages), this study employs an approach that presents predictions of a variable based on history of the variable alone. The approach, based on non-linear determinism, involves: (1) reconstruction of single-dimensional series in multi-dimensional phase-space for representing the underlying dynamics; and (2) use of the local approximation technique for prediction. For implementation, river flow and suspended sediment transport variables observed at the St. Louis (Missouri) station are studied. Specifically, daily water discharge, suspended sediment concentration and suspended sediment load data are analysed for their predictability and range, by making predictions from one day to ten days ahead. The results lead to the following conclusions: (1) extremely good one-day ahead predictions are possible for all the series; (2) prediction accuracy decreases with increasing lead time for all the series, but the decrease is much more significant for suspended sediment concentration and suspended sediment load; and (3) the number of mechanisms dominantly governing the dynamics is three for each of the series.

Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests

“…The estimations thus obtained are consistent with the data at the times of measurement, but do not allow for the estimation of uncertainty (Holtschlag, 2001). Sivakumar and Wallender (2005) advocate using a nonlinear deterministic dynamic model that builds upon a local approximation in multi-dimensional phase space. This method yielded promising results when used with comparatively densely sampled data but is also not suitable for predicting values of unmonitored events.…”

Temporal dynamics of streamflow: application of complex networks

“…This way, each point in the phase space can serve as a node in the network and the distances between the points can serve to identify the links. Such a phase space reconstruction approach for network construction is certainly appealing, especially considering that it has already proved useful for representing the temporal dynamics of streamflow (and other hydrologic processes), both in the Mississippi River basin (e.g., Sivakumar and Jayawardena 2002;Sivakumar and Wallender 2005;Sivakumar et al 2007) and in many other basins around the world (e.g., Regonda et al 2004;Salas et al 2005;Sivakumar and Singh 2012;Jothiprakash and Fathima 2013;Tongal et al 2013). Research in this direction is currently underway.…”

“…It is relevant to mention here that the temporal dynamics of streamflow (and other river-related processes) observed at the St. Louis station have been investigated by many studies in recent years. Among such studies, those that have employed nonlinear dynamic and chaos concepts for system identification, prediction, and catchment classification (e.g., Sivakumar and Jayawardena 2002;Sivakumar and Wallender 2005;Sivakumar et al 2007) may be of particular interest in the context of complex networks, as there is potential to construct networks based on nonlinear data reconstruction (phase space reconstruction). This will be addressed in a future study.…”

Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models

“…The log linear rating curve method has been used widely and Sivakumar and Wallender (2005) outline the many flaws associated with this technique, including the lack of fit due to missing variables (e.g. Miller, 1951), retransformation bias (e.g.…”

“…According to Sivakumar and Wallender (2005), the technique has been modified including, among others, use of separate curves for different seasons (Miller, 1951), stratifying the data according to the magnitude of flow and applying a separate curve for each stratum (Glysson, 1987), and use of a single multivariate model instead of multiple rating curves (Cohn et al, 1992). Sivakumar and Wallender (2005) argue that there is not a simple (and universal) 'water discharge-suspended sediment concentration-suspended sediment load' relationship. A brief overview of past studies is as follows.…”