Publication

Sampling sufficiency for determining hydraulic habitat diversity

Abstract

The geometry and hydrodynamics of river reaches are key ecohydraulic descriptors. Statistics of water depth and velocity measurements are usually taken as proxies for habitat suitability in rivers. However, little is known about the sufficiency of data to produce effective and rep-resentative results. In this research, 19 reaches with differences in terms of discharge, river width, substrate, reach length, cross-section spacing and geomorphology are investigated. Measurements of flow depth and velocity were taken at multiple, equally spaced cross-sec-tions along each reach. Data were sub-sampled using different methodologies and analysed each time. The sets of sub-sampled data were then compared with those calculated with the full data set from a reach. The focus was put towards the hydro-morphological index of diversity (HMID), a combination of the classical ecohydraulic variables flow depth and vel-ocity. It represents the spatial variability of hydraulic habitats in a reach. The results point out that, with a well-defined sampling strategy, 100 measurement points lead to a good estimation of the HMID value in a reach, if more than eight measurement points are taken per cross-section. For geomorphologies with small complexity or when the analysis only includes the estimation of mean flow depth or mean flow velocity, this number can be decreased according to the results presented here. These findings help both, aquatic ecolo-gists and engineers to estimate their data reliability for hydraulic field measurements in a river reach and are herein discussed taking into account the different studied morphologies.

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Related concepts (33)
Sampling (statistics)
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.
Computational complexity
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.
Complexity class
In computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.
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