scholarly journals A New Tool for Assessing Environmental Impacts of Altering Short-Term Flow and Water Level Regimes

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2913
Author(s):  
María Dolores Bejarano ◽  
Jaime H. García-Palacios ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote ◽  
Christer Nilsson
Keyword(s):  
River Flow ◽  
Water Levels ◽  
Computational Tool ◽  
Short Term ◽  
Stream Flows ◽  
Glen Canyon Dam ◽  
Rates Of Change ◽  
Hydrological Alteration ◽  
Dam Operation

The computational tool InSTHAn (indicators of short-term hydrological alteration) was developed to summarize data on subdaily stream flows or water levels into manageable, comprehensive and ecologically meaningful metrics, and to qualify and quantify their deviation from unaltered states. The pronunciation of the acronym refers to the recording interval of input data (i.e., instant). We compared InSTHAn with the tool COSH-Tool in a characterization of the subdaily flow variability of the Colorado River downstream from the Glen Canyon dam, and in an evaluation of the effects of the dam on this variability. Both tools captured the hydropeaking caused by a dam operation, but only InSTHAn quantified the alteration of key flow attributes, highlighting significant increases in the range of within-day flow variations and in their rates of change. This information is vital to evaluate the potential ecological consequences of the hydrological alteration, and whether they may be irreversible, making InSTHAn a key tool for river flow management.

scholarly journals Impact of Sediments and Constructions on River Flooding in Coimbra, Portugal

10.29007/v6th ◽  
2018 ◽  
Author(s):  
Jose Alfeu Sa Marques ◽  
Nuno Simoes ◽  
Lucas Maluf ◽  
Fernando Seabra Santos ◽  
Jose Vieira ◽  
...  
Keyword(s):  
River Flow ◽  
Sediment Accumulation ◽  
Hydrological Regime ◽  
River System ◽  
Water Levels ◽  
River Flooding ◽  
Operation Rules ◽  
Dam Operation ◽  
Upstream Part ◽  
Flooding Events

In Coimbra city, Portugal, the riverbanks have suffered several floods events in the past, due to its hydrological regime, the low slope and consequent lack of capacity of the Mondego River in its final 30 km. The construction of several dams in the upstream part of the river catchment has improved the use of the hydraulic capacity of the river system and reduced the number and intensity of flooding events in Coimbra. Nevertheless, intense rainfall events combined with inadequate procedures of the dam operation rules and lack of monitoring of sediments dynamics can still originate inundation in Coimbra such as those registered between 9th and 11th of January 2016. This work presents modelling scenarios demonstrating the influence of the sediment accumulation into the riverbed and its effect on the water levels. It also presents the influence that piers from a new bridge can have into the river flow dynamics.

Changes in discharge rise and fall rates applied to impact assessment of catchment land use

2009 ◽  
Vol 41 (1) ◽  
pp. 13-26 ◽  
Author(s):  
D. R. Archer ◽  
D. Climent-Soler ◽  
I. P. Holman
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Daily Rainfall ◽  
Short Term ◽  
Rates Of Change ◽  
Fall Rates ◽  
Potential Basis ◽  
Management Policies ◽  
Land Use And Management

Despite substantial evidence that land use and management can enhance flood runoff at a local scale, evidence of increased flood risk based on peak discharges is lacking in catchments greater than 10 km2. This analysis is instead based on assessing changes in short-term rates of change in discharge. The influence of land use is demonstrated first on the small Coalburn catchment where changes in rates of rise are closely related to drainage and afforestation. For the larger Axe catchment (288 km2), changes in rates of rise are investigated by comparing annual maximum and peaks over a threshold flows for different periods, by comparing rates of rise associated with given daily rainfall and by adapting the method of flow variability analysis for use of rates of change rather than flow itself. All these methods demonstrate significant changes in river flow dynamics which seem to be in parallel with land use changes even when the influence of climate variability from year to year has been taken into account. Rates of change in discharge appear to respond to land use changes and thus provide a potential basis for application to land use management policies.

scholarly journals Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 378
Author(s):  
Taeyong Kwon ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Road Networks ◽  
Monitoring Network ◽  
Conditional Entropy ◽  
Radar Data ◽  
Disaster Risk ◽  
Water Levels ◽  
The Road ◽  
Dam Operation ◽  
A Site ◽  
High Degree

Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network.

scholarly journals Physical and Biological Characterization of Ferromagnetic Fiber Networks: Effect of Fibrin Deposition on Short-Term In Vitro Responses of Human Osteoblasts

2015 ◽  
Vol 21 (3-4) ◽  
pp. 463-474 ◽  
Author(s):  
Rose L. Spear ◽  
Brajith Srigengan ◽  
Suresh Neelakantan ◽  
Wolfram Bosbach ◽  
Roger A. Brooks ◽  
...  
Keyword(s):  
Biological Characterization ◽  
Fibrin Deposition ◽  
Short Term ◽  
Human Osteoblasts ◽  
Fiber Networks

Characterization of responses to the antiandrogen flutamide in a short-term reproduction assay with the fathead minnow

2004 ◽  
Vol 70 (2) ◽  
pp. 99-110 ◽  
Author(s):  
Kathleen M. Jensen ◽  
Michael D. Kahl ◽  
Elizabeth A. Makynen ◽  
Joseph J. Korte ◽  
Richard L. Leino ◽  
...  
Keyword(s):  
Fathead Minnow ◽  
Short Term

scholarly journals (M,β)-Stability of Positive Linear Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Octavian Pastravanu ◽  
Mihaela-Hanako Matcovschi
Keyword(s):  
Linear System ◽  
Linear Systems ◽  
Transient Behavior ◽  
Short Term ◽  
Time Dynamics ◽  
The Right ◽  
Long Term Behavior ◽  
Stability Concept

The main purpose of this work is to show that the Perron-Frobenius eigenstructure of a positive linear system is involved not only in the characterization of long-term behavior (for which well-known results are available) but also in the characterization of short-term or transient behavior. We address the analysis of the short-term behavior by the help of the “(M,β)-stability” concept introduced in literature for general classes of dynamics. Our paper exploits this concept relative to Hölder vectorp-norms,1≤p≤∞, adequately weighted by scaling operators, focusing on positive linear systems. Given an asymptotically stable positive linear system, for each1≤p≤∞, we prove the existence of a scaling operator (built from the right and left Perron-Frobenius eigenvectors, with concrete expressions depending onp) that ensures the best possible values for the parametersMandβ, corresponding to an “ideal” short-term (transient) behavior. We provide results that cover both discrete- and continuous-time dynamics. Our analysis also captures the differences between the cases where the system dynamics is defined by matrices irreducible and reducible, respectively. The theoretical developments are applied to the practical study of the short-term behavior for two positive linear systems already discussed in literature by other authors.

scholarly journals Changes in short term river flow regulation and hydropeaking in Nordic rivers

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Faisal Bin Ashraf ◽  
Ali Torabi Haghighi ◽  
Joakim Riml ◽  
Knut Alfredsen ◽  
Jarkko J. Koskela ◽  
...  
Keyword(s):  
River Flow ◽  
Flow Regulation ◽  
Short Term

scholarly journals Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Sebastian C. Ibañez ◽  
Carlo Vincienzo G. Dajac ◽  
Marissa P. Liponhay ◽  
Erika Fille T. Legara ◽  
Jon Michael H. Esteban ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
The Philippines ◽  
Water Levels ◽  
Short Term ◽  
Exogenous Variables ◽  
Reservoir Water ◽  
Forecasting Performance

Forecasting reservoir water levels is essential in water supply management, impacting both operations and intervention strategies. This paper examines the short-term and long-term forecasting performance of several statistical and machine learning-based methods for predicting the water levels of the Angat Dam in the Philippines. A total of six forecasting methods are compared: naïve/persistence; seasonal mean; autoregressive integrated moving average (ARIMA); gradient boosting machines (GBM); and two deep neural networks (DNN) using a long short-term memory-based (LSTM) encoder-decoder architecture: a univariate model (DNN-U) and a multivariate model (DNN-M). Daily historical water levels from 2001 to 2021 are used in predicting future water levels. In addition, we include meteorological data (rainfall and the Oceanic Niño Index) and irrigation data as exogenous variables. To evaluate the forecast accuracy of our methods, we use a time series cross-validation approach to establish a more robust estimate of the error statistics. Our results show that our DNN-U model has the best accuracy in the 1-day-ahead scenario with a mean absolute error (MAE) and root mean square error (RMSE) of 0.2 m. In the 30-day-, 90-day-, and 180-day-ahead scenarios, the DNN-M shows the best performance with MAE (RMSE) scores of 2.9 (3.3), 5.1 (6.0), and 6.7 (8.1) meters, respectively. Additionally, we demonstrate that further improvements in performance are possible by scanning over all possible combinations of the exogenous variables and only using a subset of them as features. In summary, we provide a comprehensive framework for evaluating water level forecasting by defining a baseline accuracy, analyzing performance across multiple prediction horizons, using time series cross-validation to assess accuracy and uncertainty, and examining the effects of exogenous variables on forecasting performance. In the process, our work addresses several notable gaps in the methodologies of previous works.

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