Responses to comments

Abstract

When estimating discharges through rating curves, temporal data consistency is a critical issue. In this research, consistency in stage-discharge data is investigated using a methodology called Bidirectional Reach (BReach), which departs from a (in operational hydrology) commonly used definition of consistency. A period is considered to be consistent if no consecutive and systematic deviations from a current situation occur that exceed observational uncertainty. Therefore, the capability of a rating curve model to describe a subset of the (chronologically sorted) data is assessed in each observation by indicating the outermost data points for which the rating curve model behaves satisfactorily. These points are called the maximum left or right reach, depending on the direction of the investigation. This temporal reach should not be confused with a spatial reach (indicating a part of a river). Changes in these reaches throughout the data series indicate possible changes in data consistency and if not resolved could introduce additional errors and biases. In this research, various measurement stations in the UK, New Zealand and Belgium are selected based on their significant historical ratings information and their specific characteristics related to data consistency. For each country, regional information is maximally used to estimate observational uncertainty. Based on this uncertainty, a BReach analysis is performed and, subsequently, results are validated against available knowledge about the history and behavior of the site. For all investigated cases, the methodology provides results that appear to be consistent with this knowledge of historical changes and thus facilitates a reliable assessment of (in)consistent periods in stage-discharge measurements. This assessment is not only useful for the analysis and determination of discharge time series, but also to enhance applications based on these data (e.g., by informing hydrological and hydraulic model evaluation design about consistent time periods to analyze).

Multiple Linear Regression Modeling To Predict the Stability of Polymer–Drug Solid Dispersions: Comparison of the Effects of Polymers and Manufacturing Methods on Solid Dispersion Stability

“…However it has been observed in this study that specifically at low drug loading these two methods lack the sensitivity to detect onset of crystallinity accurately. Here PLM has therefore been used as a primary method of detection as it is known to be a sensitive and rapid method to detect small amounts of crystalline material in amorphous matrices [41][42][43] .…”

“…H-bond donors include alcohol and carboxylic acid both present in HPMCAS. Amide (PVP-VA, SOL), esters (SOL, PVP-VA and HPMCAS) and ethers (HPMCAS, SOL) can all act as H-bond acceptors 42 . Thus only HPMCAS has H-bond donors while all polymers have H-bond acceptors present.…”

Effect of Polymer Hydrophobicity on the Stability of Amorphous Solid Dispersions and Supersaturated Solutions of a Hydrophobic Pharmaceutical

“…The influence of polymer hydrophobicity on precipitation inhibition in supersaturated solutions and crystallization inhibition in amorphous solid dispersions is determined using a set of functionalized polymers for the same model pharmaceutical. There are few studies that correlate the stability of amorphous solid dispersions and the supersaturated state for a given combination of pharmaceutical and polymeric excipient; [9][10][11][12] however, effective formulation additives in amorphous solid dispersions must inhibit crystallization in both of these contexts. By controlling for physical differences among polymer and the chemical identity of crystallizing drug, the relationship between polymer hydrophobicity and stability can be understood in order to optimize the properties of polymer for long-term stability of amorphous solid dispersions.…”