A large sample analysis of seasonal river flow correlation and its physical drivers
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Citations
Understanding Hydrologic Variability across Europe through Catchment Classification
Selecting the Probability Distribution of Cone Tip Resistance Using Moment Ratio Diagram for Soil in Nasiriyah
References
Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods
Understanding Hydrologic Variability across Europe through Catchment Classification
Determination of flood seasonality from hydrological records / Détermination de la saisonnalité des crues à partir de séries hydrologiques
Comparative analysis of the seasonality of hydrological characteristics in Slovakia and Austria.
Factors influencing long range dependence in streamflow of European rivers
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Frequently Asked Questions (10)
Q2. How is the selection of a seasonal metric used?
To evaluate the selection of HFS, a metric constructed as the percentage of annual maximum flows (PAMF) captured in the HFS is used.
Q3. What is the potential limitation of the seasonal correlations?
A potential limitation is the assumption of symmetrical extension of HFS around the peak month, along with the uniform selection of its length (3-month period).
Q4. What is the probability distribution of the conditioned peak flows?
After application of the inverse NQT the conditioned peak flows are modelled through the EV1 distribution and compared to the unconditioned (observed) peak flows.
Q5. What is the standard normal quantile for each cumulative frequency?
(b) the cumulative frequency, e.g. FQmi , is computed via a Weibull plotting position, and (c) the standard normal quantile, e.g. NQmi , is obtained as the inverse of the standard normal distribution for each cumulative frequency, e.g. G−1 (FQmi ).
Q6. What is the description of the studied rivers?
1. A summary of the river basins under study, in terms of the selected descriptors, is also provided in Table 1, showing that the investigated rivers cover a wide range of catchment area sizes, flow regimes, and climatic conditions.
Q7. What is the way to compare the seasonal correlations of the catchments?
By focusing on HFS, one can notice that the catchments with higher seasonal correlation are characterized by larger catchment area; higher baseflow index and temperature with respect to the remaining catchments; and lower specific runoff, precipitation, and wetness.
Q8. What is the effect of increased wetness on seasonal memory?
In fact, their finding that increased wetness has a negative impact on seasonal memory of both high and low flows extends the above results to the seasonal scale and, interestingly, to both types of extremes.
Q9. What is the main conclusion of Kuentz et al. (2017)?
In this respect, Kuentz et al. (2017) found that topography exerts dominant controls over the flow regime in the larger European region, controlling the flashiness of flow and being a particularly important driver for other low-flow signatures too.
Q10. What are the six groups of potential drivers of seasonal correlation magnitude?
To attribute the detected correlations to physical drivers, the authors define six groups of potential drivers of seasonal correlation magnitude: basin size, flow indices, the presence of lakes and glaciers, catchment elevation, catchment geology, and hydroclimatic forcing.