scholarly journals Various far-field hydrological responses during 2015 Gorkha earthquake at two distant wells

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xudong Huang ◽  
Yu Zhang
Keyword(s):  
Water Level ◽  
Seismic Waves ◽  
Amplitude Ratio ◽  
Water Levels ◽  
Earth Tides ◽  
Tibet Plateau ◽  
Far Field ◽  
Short Term ◽  
Gorkha Earthquake ◽  
2015 Gorkha Earthquake

AbstractAquifer hydraulic parameter can change during earthquakes. Continuous monitoring of the response of water level to seismic waves or solid Earth tides provides an opportunity to document how earthquakes influence hydrological properties. Here, we use data of two groundwater wells, Dian-22 (D22) and Lijiang (LJ) well, in southeast Tibet Plateau in response to the 2015 Mw 7.8 Gorkha earthquake to illustrate hydrological implications. The coherences of water level and seismic wave before and after the far-field earthquake show systematic variations, which may confirm the coseismic dynamic shaking influence at high frequencies (f > 8 cpd). The tidal response of water levels in these wells shows abrupt coseismic changes of both phase shift and amplitude ratio after the earthquake, which may be interpreted as an occurrence in the vertical permeability of a switched semiconfined aquifer in the D22 well, or an enhancement unconfined aquifer in the LJ well. Using the continuous short-term transmissivity monitoring, we show that the possible coseismic response for about 10 days and instant healing after 10 days to the causal earthquake impact. Thus, the dynamic shaking during the Gorkha earthquake may have caused the short-term aquifer responses by reopening of preexisting vertical fractures and later healing at epicentral distances about 1500 km.

scholarly journals Various far-field hydrological responses during 2015 Gorkha earthquake at two distant wells

2020 ◽  
Author(s):  
Xudong Huang ◽  
Yu Zhang
Keyword(s):  
Water Level ◽  
Seismic Waves ◽  
Amplitude Ratio ◽  
Unconfined Aquifer ◽  
Water Levels ◽  
Earth Tides ◽  
Tibet Plateau ◽  
Far Field ◽  
Gorkha Earthquake ◽  
2015 Gorkha Earthquake

Abstract Aquifer hydraulic parameter can change during earthquakes. Continuous monitoring of the response of water level to seismic waves or solid Earth tides provides an opportunity to document how earthquakes influence hydrological properties. Here we use data of two groundwater wells, Dian-22 (D22) and Lijiang (LJ) well, in southeast Tibet Plateau in response to the 2015 Mw 7.8 Gorkha earthquake to illustrate hydrological implications. The coherences of water level and seismic wave before and after the far-field earthquake show systematic variations, which may confirm the coseismic dynamic shaking influence at high frequencies (f > 8 cpd). The tidal response of water levels in these wells shows abrupt coseismic changes of both phase shift and amplitude ratio after the earthquake, which may be interpreted as an occurrence in the vertical permeability of a switched semiconfined aquifer in the D22 well, or an enhancement unconfined aquifer in the LJ well . Using the continuous transmissivity monitoring, we show that the possible coseismic response for about 10 days and instant healing after 10 days to the earthquake. Thus, the dynamic shaking during the Gorkha earthquake may have caused the short term aquifer responses by reopening of preexisting vertical fractures and later healing at epicentral distances about 1500 km.

scholarly journals Various Far-Field Hydrological Responses During 2015 Gorkha Earthquake at Two Distant Wells

2020 ◽  
Author(s):  
Xudong Huang ◽  
Yu Zhang
Keyword(s):  
Water Level ◽  
Seismic Waves ◽  
Amplitude Ratio ◽  
Epicentral Distance ◽  
Water Levels ◽  
Earth Tides ◽  
Tibet Plateau ◽  
Far Field ◽  
Gorkha Earthquake ◽  
2015 Gorkha Earthquake

Abstract Aquifer hydraulic parameter can change during earthquakes. Continuous monitoring of the response of water level to seismic waves or solid Earth tides provides an opportunity to document how earthquakes influence hydrological properties. Here we use data of two groundwater wells, Dian-22 (D22) and Lijiang (LJ) well, in southeast Tibet Plateau in response to the 2015 Mw 7.8 Gorkha earthquake to illustrate hydrological implications. The coherences of water level and seismic wave before and after the far-field earthquake show systematic variations, which may confirm the coseismic dynamic shaking influence at high frequencies (f > 8 cpd). The tidal response of water levels in these wells shows abrupt coseismic increases of both phase shift and amplitude ratio after the earthquake, which may be interpreted as an increase in the horizontal permeability of a confined aquifer in D22 well, and an occurrence in the vertical permeability of a switched semiconfined aquifer with larger epicentral distance and but high seismic ground motion. Using the continuous transmissivity monitoring, we show that the possible preseismic initial for ~ 1 day, coseismic response for ~ 3 days and postseismic healing for ~ 10 days during the earthquake. Thus, the dynamic shaking during the Gorkha earthquake may have caused confined aquifers to semiconfined aquifers by reopening of preexisting vertical fractures and later healing at epicentral distances about 1500 km.

Short-term growth (RNA/DNA ratio) of yellow perch (Perca flavescens) in relation to environmental influences and spatio-temporal variation in a shallow fluvial lake

2007 ◽  
Vol 64 (12) ◽  
pp. 1646-1655 ◽  
Author(s):  
Hélène Glémet ◽  
Marco A Rodríguez
Keyword(s):  
Temporal Variation ◽  
Water Level ◽  
Yellow Perch ◽  
Perca Flavescens ◽  
Negative Influence ◽  
Water Levels ◽  
Short Term ◽  
Ecological Processes ◽  
Flood Pulse Concept ◽  
Spatio Temporal

Shallow fluvial lakes are heterogeneous ecosystems in which marked spatio-temporal variation renders difficult the analysis of key ecological processes, such as growth. In this study, we used generalized additive modelling of the RNA/DNA ratio, an index of short-term growth, to investigate the influence of environmental variables and spatio-temporal variation on growth of yellow perch (Perca flavescens) in Lake St. Pierre, Quebec, Canada. Temperature and water level had seemingly stronger effects on short-term growth than seasonal change or spatial variation between and along the lakeshores. Consistent with previous studies, the maximum RNA/DNA ratio was found at 20.5 °C, suggesting that our approach provides a useful tool for estimating thermal optima for growth in the field. The RNA/DNA ratio showed a positive relationship with water level, as predicted by the flood pulse concept, a finding with implications for ecosystem productivity in fluvial lakes. The RNA/DNA ratio was more variable along the north than the south shore, possibly reflecting exposure to more differentiated water masses. The negative influence of both high temperatures and low water levels on growth points to potential impacts of climatic change on fish production in shallow fluvial lakes.

scholarly journals The influence of long- and short-term volcanic strain on aquifer pressure: a case study from Soufrière Hills Volcano, Montserrat (W.I.)

2020 ◽  
Vol 223 (2) ◽  
pp. 1288-1303
Author(s):  
K Strehlow ◽  
J Gottsmann ◽  
A Rust ◽  
S Hautmann ◽  
B Hemmings
Keyword(s):  
Water Level ◽  
Pore Pressure ◽  
Crustal Deformation ◽  
Water Levels ◽  
Short Term ◽  
Soufriere Hills Volcano ◽  
Soufrière Hills Volcano ◽  
Water Level Changes ◽  
Soufriere Hills

Summary Aquifers are poroelastic bodies that respond to strain by changes in pore pressure. Crustal deformation due to volcanic processes induces pore pressure variations that are mirrored in well water levels. Here, we investigate water level changes in the Belham valley on Montserrat over the course of 2 yr (2004–2006). Using finite element analysis, we simulate crustal deformation due to different volcanic strain sources and the dynamic poroelastic aquifer response. While some additional hydrological drivers cannot be excluded, we suggest that a poroelastic strain response of the aquifer system in the Belham valley is a possible explanation for the observed water level changes. According to our simulations, the shallow Belham aquifer responds to a steadily increasing sediment load due to repeated lahar sedimentation in the valley with rising aquifer pressures. A wholesale dome collapse in May 2006 on the other hand induced dilatational strain and thereby a short-term water level drop in a deeper-seated aquifer, which caused groundwater leakage from the Belham aquifer and thereby induced a delayed water level fall in the wells. The system thus responded to both gradual and rapid transient strain associated with the eruption of Soufrière Hills Volcano (Montserrat). This case study gives field evidence for theoretical predictions on volcanic drivers behind hydrological transients, demonstrating the potential of hydrological data for volcano monitoring. Interrogation of such data can provide valuable constraints on stress evolution in volcanic systems and therefore complement other monitoring systems. The presented models and inferred results are conceptually applicable to volcanic areas worldwide.

scholarly journals Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study

2013 ◽  
Vol 16 (1) ◽  
pp. 218-230 ◽  
Author(s):  
Gooyong Lee ◽  
Sangeun Lee ◽  
Heekyung Park
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Level ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
River Management ◽  
Water Levels ◽  
Short Term

This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water levels at 15 points of four major rivers in Korea. This case study demonstrates that the approach can be highly compatible with the real river situations, such as hydrological disturbances and water infrastructure under emergencies. Therefore, proper adoption of this approach into a river management system certainly improves the adaptive capacity of the system.

scholarly journals Geoinformation Modeling of Flooded Areas in Settlements (in the Example of Lutsk)

2018 ◽  
Author(s):  
Anna Shostak ◽  
Volodymyr Voloshyn ◽  
Oleksandr Melnyk ◽  
Pavlo Manko
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Practical Significance ◽  
Natural Phenomenon ◽  
Short Term ◽  
Sensing Data ◽  
Geoinformation Modeling ◽  
Flooded Areas

Object. Flooding in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous. In an average year floods on the rivers of Volyn region take place from one to three times which extend beyond the limits of the floodplain. The floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Methodology. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. Based on the statistical data of the Volyn Regional Center for Hydrometeorology during the 7 year period 2011-2017 about water levels of the river Styr. We conducted mathematical modeling of fluctuations of water levels within the territory of Lutsk, based on creating a partial Fourier series for discrete values of middle-ten-day water levels values. The post hydrological measurements of Styr river water levels in the territory of Lutsk located on the Shevchenko Street comply with an altitude 172.87 meters. Based on the data of short-term flood forecasting in February and March, and relief data from the Department of Architecture and Urban Development of Volyn State Administration, we conducted visualization of the results using geographic information system QGIS. Results. The results of mathematical processing were the basis for geoinformation simulation of flooded areas using remote sensing data that are publicly available. Use of statistical and geospatial data in this article has great potential for further application in modeling the processes of natural and technogenic origin. Scientific novelty. The mathematical model of short-term forecasting of water levels during the flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed. Practical significance. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The spring flood in February-March 2018, with the maximum water level 5.33 m, corresponds to an absolute mark of 178.20 m, which is forecasted in this article.

scholarly journals Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

2021 ◽  
Vol 18 (17) ◽  
pp. 9287
Author(s):  
Yu Liu ◽  
Hao Wang ◽  
Wenwen Feng ◽  
Haocheng Huang
Keyword(s):  
Water Level ◽  
Real Time ◽  
River Water ◽  
Urban Areas ◽  
Urban River ◽  
Water Levels ◽  
Urban Water ◽  
Short Term ◽  
Forecast Performance ◽  
Flood Season

Water level management is an important part of urban water system management. In flood season, the river should be controlled to ensure the ecological and landscape water level. In non-flood season, the water level should be lowered to ensure smooth drainage. In urban areas, the response of the river water level to rainfall and artificial regulation is relatively rapid and strong. Therefore, building a mathematical model to forecast the short-term trend of urban river water levels can provide a scientific basis for decision makers and is of great significance for the management of urban water systems. With a focus on the high uncertainty of urban river water level prediction, a real-time rolling forecast method for the short-term water levels of urban internal rivers and external rivers was constructed, based on long short-term memory (LSTM). Fuzhou City, China was used as the research area, and the forecast performance of LSTM was analyzed. The results confirm the feasibility of LSTM in real-time rolling forecasting of water levels. The absolute errors at different times in each forecast were compared, and the various characteristics and causes of the errors in the forecast process were analyzed. The forecast performance of LSTM under different rolling intervals and different forecast periods was compared, and the recommended values are provided as a reference for the construction of local operational forecast systems.

scholarly journals Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 55
Author(s):  
Tsumugu Kusudo ◽  
Atsushi Yamamoto ◽  
Masaomi Kimura ◽  
Yutaka Matsuno
Keyword(s):  
Water Management ◽  
Deep Learning ◽  
Water Level ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Water Levels ◽  
Short Term ◽  
Term Memory ◽  
Deep Learning Algorithm ◽  
Long Short Term Memory

In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash–Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.

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