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14 results for “rating curve”
Rating curves based on satellite altimetry and in-situ discharge data
<h1>Context: </h1> <p>The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV.</p> <h1>Data description :</h1> <p>Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series:</p> <p>⋅ <strong><em>Method 1</em>: </strong>The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period.</p> <p>⋅ <em><strong>Method 2:</strong></em> The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q).</p> <h1>Approaches to derive Rating Curve (RC) :</h1> <h2>Bayesian Approach :</h2> <p>The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates. </p> <p>According to this, the estimation of the rating curve using the Bayesian method involves several steps:</p> <ul> <li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li> </ul> <p><em> Q = a⋅(WSE-z</em><em>0</em><em>)</em><sup><em>b</em></sup></p> <p>Here, <em>a, z0</em> and <em>b</em> are the parameters of the rating curve. <em>a,</em> is a scaling coefficient governing the magnitude of the Q-WSE relationship, <em>b,</em> characterizes the nature of this relationship, and <em>z0</em>, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959).</p> <ul> <li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li> </ul> <p><u>- Coefficient “a”</u>: adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S<sup>1/2</sup> (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:</p> <p> a ∈ [0; 3000]</p> <p><u>- Coefficient “b”</u>: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:</p> <p> b ∈ [0; 5]</p> <p><u>- Coefficient “z0”</u>: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this <em>reason, the value</em> cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:</p> <p> z0 ∈ [min(WSE)-30; min(WSE)]</p> <ul> <li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li> </ul> <h2>Quantile approach : </h2> <p>The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013).</p> <p>Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously.</p> <h1>File description :</h1> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>basin-station</td> <td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td> </tr> <tr> <td>lon</td> <td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td> </tr> <tr> <td>lat</td> <td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td> </tr> <tr> <td>a</td> <td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td> </tr> <tr> <td>b</td> <td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td> </tr> <tr> <td>z0</td> <td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td> </tr> <tr> <td>a_sd</td> <td>Standard deviation of the coefficient "a". Number with 3 decimals.</td> </tr> <tr> <td>b_sd</td> <td>Standard deviation of the coefficient "b". Number with 3 decimals.</td> </tr> <tr> <td>z0_sd</td> <td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td> </tr> <tr> <td>period</td> <td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td> </tr> <tr> <td>nb</td> <td>Number of overlap dates to compute the rating curve.</td> </tr> <tr> <td>Methodology</td> <td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td> </tr> <tr> <td>Source</td> <td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td> </tr> </tbody> </table> <p>---------</p> <p><em>THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR </em><em>IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</em><br><em>FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE </em><em>AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER </em><em>LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, </em><em>OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE </em><em>DATASET.</em></p>
Preference ratings and 32 magnitude frequency response curves
<p>This repository comprises two CSV files: PreferenceRatings and MagnitudeFrequencyResponses. The former includes preference ratings obtained from 56 naive assessors (30 Danish---DK, 26 Japanese---JP) of 32 headphone curves over several music programs in several trials. The latter includes the magnitude frequency response curves evaluated by the assessors, expressed as gains of a 30-band graphic equalizer whose bands are centered between 31 Hz and 25 kHz. The curves were either derived from eight popular closed circumaural headphones, measured with a Brüel & Kjær Head and Torso Simulator 5128C, or otherwise obtained from the literature. The details of the methods, results, etc. are published in [1].</p> <p>[1] G. Ravizza, J. Villegas, T. Stegenborg-Andersen, and C. P. Volk, “An over-ear headphone target curve for Brüel & Kjær head and torso simulator type 5128 measurements,” in Proc. 155 Audio Eng. Soc. Conv., Oct 2023.</p> <p> </p>
Supplement to "Proof of concept for Bayesian inference of dynamic rating curve uncertainty" (v3)
<div>This deposit contains part of the updated supplement to “Proof of concept for Bayesian inference of dynamic rating curve uncertainty” (<a href="https://www.tandfonline.com/doi/full/10.1080/02626667.2024.2401094" target="_blank" rel="noopener">Cornelio et al. 2024, HSJ</a>). This version, in particular, contains two files in which the following changes were made from the earlier version (v2.0.1):</div> <div> <ul> <li><strong><em>250117_Lbn_RC_new.R</em></strong> is the updated R code. The argument for the random number generator (RNG) kind is defined for the set.seed() functions used in the script. </li> <li><strong><em>Lbn-DMs-csv0.csv</em></strong> is the updated input file containing the stage-discharge gaugings. The column for the stage values has been renamed to "H_rec" (instead of "H_m" as in the original CSV) to be consistent with the attribute name used throughout the R code.</li> </ul> <p>Except for the above files, all the input and output files in <a href="https://zenodo.org/records/12792513" target="_blank" rel="noopener">v2.0.1</a><span> remain unchanged. </span></p> </div> <p><u> </u></p>
Rating Curves for Lakes Azuei and Enriquillo
<p>The EXCEL contains the data for ratings curves of Lakes Azuei (Haiti) and Enriquillo (Dominican Republic). The rating curves are for Volume, Surface Area, and Depth (at the deepest point). They were used for computing volume changes based on surface area extension deduced from LandSat imagery. The data is supported through a number of graphical representations of the rating curves. </p>
Fig. S4. Growth curves for A.intermedia when started the experiment with a in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S4. Growth curves for A.intermedia when started the experiment with a single cell. Color points represents each one of the single-cell experiments, color legend is in the left corner of the figure. Black line correspond to the average growth between experiments.
Dataset for submission of "Correction of river bathymetry parameters using the stage–discharge rating curve"
<p>Dataset for submission of "Correction of river bathymetry parameters using the stage–discharge rating curve". </p>
France Treasury Instantaneous Forward Rate Curves
<p>French forward rate curves from October 22, 1987, through October 20, 2023. The file contains the French forward rate curves we can construct using a Svensson methodology and <em>all</em> available public data of French nominal government debt securities<em> </em>called OATs (<em>Obligations Assimilables du Trésor</em>) from October 22, 1987, throughOctober 20, 2023. Please consult Grishchenko, Moraux and Pakulyak (2020) for extended details on the methodology, model fit, etc.</p>
Dataset for the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan"
<p>This is the dataset for developing the HAND-hd workflow mentione in the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan". In the manuscript, we used the topographical cross-sectional survey data to generate topographical synthetic rating curves (RCb) to validate the performance of the HAND method based synthetic rating curves (HAND-SRC) produced by the HAND-hd workflow. Due to regulatory restrictions under Taiwanese law, public sharing of 5-meter (or finer) DEM data is prohibited. However, researchers interested in accessing processed HAND raster data for research purposes may contact the corresponding author.</p>
Light Curves and Event Rates of Axion Instability Supernovae
<p>These files are MESA (Modules for Experiments in Stellar Astrophysics) inlists that can reproduce results in Mori et al. (2022) (https://arxiv.org/abs/2209.03517). MESA version 12778 and MESA SDK version x86_64-linux-20.3.2 are used. Before running the code, the user should edit MESA following the instruction in Sakstein, Croon & McDermott (https://doi.org/10.5281/zenodo.6347632).</p> <p>PISN.tar.gz includes inlists for standard pair-instability supernovae while AISN_0.5.tar.gz and AISN_2.0.tar.gz are for axion instability supernovae with f=0.5 (i.e. ALP mass = 511 keV) and f=2.0 (i.e. ALP mass = 511*4 keV), respectively. Each directory consists of directories like "heavy_new_particle_100". The three digit number indicates the initial stellar mass.</p> <p>The users are encouraged to cite the following papers when they write a paper using these inlists.</p> <ul> <li>Mori et al. (2022), arXiv:2209.03517</li> <li>Sakstein, Croon & McDermott (2022) Phys. Rev. D <strong>105</strong>, 095038</li> <li>Croon, McDermott & Sakstein (2020) Phys. Rev. D <strong>102</strong>, 115024</li> <li>MESA instrumental papers</li> </ul>
Evaluated hydrodynamic-model based rating curves at virtual stations in Mahanadi river basin
<p>The hydrodynamic model, Hydrologic Engineering Centre - River Analysis System (HEC-RAS), was setup and 07 virtual stations were identified for Mahanadi River from Boudh to Mundali Barrage. Rating curves were generated at these virtual stations through model simulations, and their accuracy was validated using multi-mission altimetry data. These rating curves serve as a cost-effective solution for monitoring river flows at additional locations, enabling the production of discharge time series for various hydrological applications and facilitating the assessment of contributions from lateral tributaries.</p> <p> The rating curves data is uploaded here, which can be utilized for various hydrological applications.</p>
Fuel up with OATmeals! Forward Rate Curves
<p>French forward rate curves from October 22, 1987, through April 10, 2018.</p>
Data from: Beyond thermal performance curves: modeling time-dependent effects of thermal stress on ectotherm growth rates
Thermal performance curves have been widely used to model the ecological responses of ectotherms to variable thermal environments and climate change. Such models ignore the effects of time dependence—the temporal pattern and duration of temperature exposure—on performance. We developed and solved a simple mathematical model for growth rate of ectotherms, combining thermal performance curves for ingestion rate with the temporal dynamics of gene expression and protein production in response to high temperatures to predict temporal patterns of growth rate in constant and diurnally fluctuating temperatures. We used the model to explore the effects of heat shock proteins on larval growth rates of Manduca sexta. The model correctly captures two empirical patterns for larval growth rate: first, maximal growth rate and optimal temperature decline with increasing duration of temperature exposure; second, mean growth rates decline with time in diurnally fluctuating temperatures at higher mean temperatures. These qualitative results apply broadly to cases where proteins or other molecules produced in response to high temperatures reduce growth rates. We discuss some of the critical assumptions and predictions of the model and suggest potential extensions and alternatives. Incorporating time-dependent effects will be essential for making more realistic predictions about the physiological and ecological consequences of temperature fluctuations and climate change.
A Study to Assess Plasma Ammonia Time-Normalized Area Under the Curve and Rate of Ureagenesis in Healthy Adult Subjects
ClinicalTrials.gov study NCT04269122. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Beyond thermal performance curves: modeling time-dependent effects of thermal stress on ectotherm growth rates
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