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5 results for “Observed river discharge”
Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms
<p><strong>This repository provides three data files in CSV format:</strong><br> 1. Gauge name, lat/lon information<br> 2. Gauge name, date, and multi-temporal river width extracted from Landsat<br> 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors</p> <p>Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. <em>RSE</em>.</p> <p> </p> <p>Updated: 2022/6/17, 2023/1/19</p> <p> </p>
Optimal copula models for the observed discharge and nitrate concentration in the Lower Illinois River
<p>Investigating the complex relationships between water quality parameters and river discharge has been an area of active research for decades. The objective of this study is to apply bivariate distributions to river nitrate concentration and discharge and to suggest the procedure of choosing the best bivariate distribution in the selected models. Nitrate concentration (NO<sub>3</sub>) data were measured from 1972 to 2012 at five stations in the Lower Illinois River basin, USA. The bivariate distribution was represented by applying three copula models to explore the dependence between discharge and nitrate concentration. The marginal distributions for the copula models include the generalized Pareto (GPA), generalized extreme value (GEV), two-parameter lognormal (LN2), and three-parameter lognormal (LN3) distributions as well as the three copula models of Clayton, Frank, and Gumbel, respectively. Each procedure was tested by the robust diagnostic, the probability plot correlation coefficient (PPCC) test, and the goodness-of-fit test ( statistic). Consequently, the Frank copula model with GPA for stream flow–GPA for nitrate concentration performed more accurately for data from the mainstream stations, Havana (D-31) and Valley City (D-32) and for those from a station on one tributary, Oakford station along the Sangamon River (E-25). However, along other tributaries such as the La Moine River at Ripley (DG-01) and Spoon River at Seville (DJ-08), the Clayton copula model with NL3 for stream flow–GPA for nitrate concentration showed better fit. This study enabled us to develop a procedure to determine an optimal copula for application to the environmental sciences and suggest a case study for the application of copula models to discharge, as a hydrologic variable, and nutrient concentration as a water quality variable.</p>
Modeled and observed river water temperature and discharge in the paper "Riverine heat waves on the rise, outpacing air heat waves"
<div>The observed and modeled data – mean daily water temperature (WT, °C) and mean daily discharge (Q,<em> </em>ft<sup>3</sup>/s) modeled by an LSTM model (averaged over 5 model runs) – for 1471 sites over 1980-2022 can be found here. Out of these 1471 sites, 1276 sites had good model performance and were used to identify and analyse air and riverine heat waves (RHW) in the paper "Riverine heat waves on the rise, outpacing air heat waves". </div> <div> <div> </div> </div>
At-a-station hydraulic geometry exponent b derived from Landsat river width and discharge observation
<h2>Description</h2> <p>Global at-a-station hydraulic geometry exponent b dataset derived from Landsat river width (Feng et al., 2022) and discharge observation (Lin et al., 2019).</p> <p> </p> <p>For more details, please refer to:</p> <div> </div> <p><span>Zimin Yuan, Peirong Lin, Xiwei Guo, Kai Zhang, Hylke E. Beck. Revisiting At-a-Station Hydraulic Geometry Using Discharge Observations and Satellite-Derived River Widths. <em>J Remote Sens.</em> 2024;4:0271. DOI:<a href="https://doi.org/10.34133/remotesensing.0271">10.34133/remotesensing.0271</a></span></p> <p> </p> <h2>Contacts</h2> <ul> <li>Zimin Yuan, <a href="mailto:ziminyuan@pku.edu.cn">ziminyuan@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Dataset of observed streamflow and calculated base flow, groundwater runoff and groundwater discharge of the Zhizdra River, Russia (1958–2016)
<p>This dataset accompanies the paper, titled “A physically based model of a two-pass digital filter for groundwater runoff separation from streamflow time series”, submitted to Water Resources Research.</p> <p>The dataset includes the daily river runoff of the Zhizdra River, Russia, during 1958–2016. Using this dataset, we validate the proposed two-pass digital filter model to separate groundwater runoff from streamflow. This dataset also includes the calculated base flow, groundwater runoff and groundwater discharge to the Zhizdra River, Russia, during 1958–2016.</p>
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