scholarly journals Seasonal and Annual Probabilistic Forecasting of Water Levels in Large Lakes (Case Study of the Ladoga Lake)

2021 ◽  
Vol 82 ◽  
pp. 13-19
Author(s):  
Natalia V. Myakisheva ◽  
Ekaterina V. Gaidukova ◽  
Sergei V. Shanochkin ◽  
Anna A. Batmazova
Keyword(s):  
Water Level ◽  
Moving Average ◽  
Water Levels ◽  
Probabilistic Forecasting ◽  
Large Lakes ◽  
Annual Variations ◽  
Ladoga Lake ◽  
Moving Average Processes ◽  
Relative Errors

The production functions of water-dependent sectors of the economy can include the water level in the lake as a natural resource. This characteristic must be able to reliably predict for the effective functioning of sectors of the economy. In the article the main attention is paid to the methods of forecasting based on the extrapolation of natural variations of the large lakes water level. As an example, is considered. In this paper, it is assumed that the level varies accordingly to a stochastic multi-cycle process with principal energy-containing zones in frequency bands associated with seasonal and multi-annual variations. Hence, the multi-year monthly and yearly averaged time series are represented by the ARIMA (auto-regression integrated moving average) processes. Forecasts are generated by using of the seasonal ARIMA-models, which take into account not only the seasonal but also the evolution non-stationarity. To compare the forecasts and the actual values, the relative errors are computed. It is shown that implementation of the models mainly allows receiving good and excellent forecasts. Subject Classification Numbers: UDC 556.555.2.06(4)

scholarly journals Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

2021 ◽  
Vol 13 (19) ◽  
pp. 10720
Author(s):  
Muhammad Ali Musarat ◽  
Wesam Salah Alaloul ◽  
Muhammad Babar Ali Rabbani ◽  
Mujahid Ali ◽  
Muhammad Altaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
River Flow ◽  
Early Stage ◽  
Moving Average ◽  
Weather Prediction ◽  
Maximum Level ◽  
Information Criterion ◽  
Water Levels ◽  
Kabul River

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

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 Validation of Sentinel-3A Based Lake Level over US and Canada

2020 ◽  
Vol 12 (17) ◽  
pp. 2835
Author(s):  
Karina Nielsen ◽  
Ole Baltazar Andersen ◽  
Heidi Ranndal
Keyword(s):  
Water Level ◽  
Satellite Altimetry ◽  
Lake Level ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
The United States ◽  
Water Levels ◽  
Winter Period ◽  
Large Lakes ◽  
In Situ Data

Satellite altimetry is an important contributor for measuring the water level of continental water bodies. The technique has been applied for almost three decades. In this period the data quality has increased and the applications have evolved from the study of a few large lakes and rivers, to near global applications at various scales. Products from current satellite altimetry missions should be validated to continuously improve the measurements. Sentinel-3A has been operating since 2016 and is the first mission operating in synthetic aperture radar (SAR) mode globally. Here we evaluate its performance in capturing lake level variations based on a physical and an empirical retracker provided in the official level 2 product. The validation is performed for more than 100 lakes in the United States and Canada where the altimetry based water levels are compared with in situ data. As validation measures we consider the root mean squared error, the Pearson correlation, and the percentage of outliers. For the US sites the median of the RMSE value is 25 cm and 19 cm and the median of the Pearson correlations are 0.86 and 0.93 for the physical and empirical retracker, respectively. The percentage of outliers (median) is 11% for both retrackers. The validations measures are slightly poorer for the Canadian sites; the median RMSE is approximately 5 cm larger, the Pearson correlation 0.1 lower, and the percentage of outliers 5% larger. The poorer performance for the Canadian sites is mainly related to the presence of lake ice in the winter period where the surface elevations are not able to map the surface correctly. The validation measures improve considerably when evaluated for summer data only. For both areas we show that the reconstruction of the water level variations based on the empirical retracker is significantly better compared to that of the physical retracker in terms of the RMSE and the Pearson correlation.

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 Instrumentation issues and problems in earth dams (Case Study; Shah Qasim Dam in Yasouj, Iran)

2020 ◽  
Vol 33 (02) ◽  
pp. 737-745
Author(s):  
Amir Behshad
Keyword(s):  
Water Level ◽  
Water Levels ◽  
Critical Conditions ◽  
Earth Dams ◽  
Water Level Changes ◽  
East Of Iran ◽  
Accurate Comparison ◽  
Safety And Stability ◽  
Over Time

Installation and monitoring of instrumentation is one of the practical methods for controlling safety and stability of earth dams. Piezometers existing in dam body and dam abutments are one of the various types of precision instruments used in dams, which indicate the height of water level in different parts of the dam. In order to evaluate the performance of piezometers of Shah Qasim Dam in Kohgiluyeh and Boyerahmad province (in south east of Iran), we compare water level changes in piezometer and water level changes in the dam lake over time. In this paper, the above mentioned dam is modelled using the SEEP/W software, then after imposing boundary conditions, water levels are computed at various points. For more accurate comparison, water level changes are plotted in transverse and longitudinal piezometers over time. The results of analysis indicate significant increase of permeability in vicinity of some piezometers. The piezometers BX4, BX14, BX13 and SP6, and the region near them, as well as piezometers SP24 and SP18 and their surrounding area, have critical conditions which should be inspected as soon as possible.

scholarly journals Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China

2016 ◽  
Vol 47 (S1) ◽  
pp. 69-83 ◽  
Author(s):  
Bing Li ◽  
Guishan Yang ◽  
Rongrong Wan ◽  
Xue Dai ◽  
Yanhui Zhang
Keyword(s):  
Water Level ◽  
Random Forests ◽  
Lake Water ◽  
Poyang Lake ◽  
Time Lag ◽  
Water Levels ◽  
Support Vector ◽  
Lake Water Level ◽  
Water Level Variations

Modeling of hydrological time series is essential for sustainable development and management of lake water resources. This study aims to develop an efficient model for forecasting lake water level variations, exemplified by the Poyang Lake (China) case study. A random forests (RF) model was first applied and compared with artificial neural networks, support vector regression, and a linear model. Three scenarios were adopted to investigate the effect of time lag and previous water levels as model inputs for real-time forecasting. Variable importance was then analyzed to evaluate the influence of each predictor for water level variations. Results indicated that the RF model exhibits the best performance for daily forecasting in terms of root mean square error (RMSE) and coefficient of determination (R2). Moreover, the highest accuracy was achieved using discharge series at 4-day-ahead and the average water level over the previous week as model inputs, with an average RMSE of 0.25 m for five stations within the lake. In addition, the previous water level was the most efficient predictor for water level forecasting, followed by discharge from the Yangtze River. Based on the performance of the soft computing methods, RF can be calibrated to provide information or simulation scenarios for water management and decision-making.

scholarly journals Natural and anthropogenic conditions of water level fluctuations in lakes – Lake Powidzkie case study (Central-Western Poland)

2019 ◽  
Vol 40 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Bogumił M. Nowak ◽  
Mariusz Ptak
Keyword(s):  
Water Level ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Anthropogenic Factors ◽  
Water Shortages ◽  
Natural Factors ◽  
Precipitation Increase ◽  
Water Outflow

Abstract The article presents the analysis of water level fluctuations in Lake Powidzkie in the years 1961–2015. The study shows a considerable decrease in mean water levels in the aforementioned multiannual period, averaging 9 cmꞏdecade−1. Such a situation is caused by natural as well as anthropogenic factors, co-determining water relations in the study area. The natural factors include the amount and distribution of precipitation, increase in air temperature and evaporation size, unfavourable relations between the lake and catchment or hydrogeological conditions. Anthropogenic factors particularly include long-term transformations of the natural environment in the region, currently associated with meliorations accompanying the nearby opencast brown coal mines and exploitation of groundwaters for municipal purposes. Water shortages occurring during dry periods were shown not to be compensated in the study area in humid years. This is particularly related to the regional lowering of the aquifer remaining in close relations with Lake Powidzkie. Counteracting the unfavourable hydrological situation is done through hydrotechnical infrastructure which partially limits water outflow from the lake through damming.

scholarly journals Multivariate statistical modelling of extreme coastal water levels and the effect of climate variability: a case study in the Netherlands

2020 ◽  
Author(s):  
Victor M. Santos ◽  
Mercè Casas-Prat ◽  
Benjamin Poschlod ◽  
Elisa Ragno ◽  
Bart van den Hurk ◽  
...  
Keyword(s):  
Time Series ◽  
Water Level ◽  
Climate Model ◽  
Regional Climate ◽  
Water Levels ◽  
Multivariate Statistical ◽  
Inland Water ◽  
Impact Function ◽  
The Impact

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a case study in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present and test a multivariate statistical framework to replicate the demonstrated dependence and the resulting return periods of inland water levels. We use the same 800-year data series to develop an impact function, which is able to empirically describe the relationship between high inland water levels (the impact) and its driving variables (precipitation and surge). In our study area, this relationship is complex because of the high degree of human management affecting the dynamics of the water level. By event sampling and conditioning the drivers, an impact function was created that can reproduce the water levels maintaining an unbiased performance at the full range of simulated water levels. The dependence structure between the driving variables is modeled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. The proposed framework is therefore able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50 year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

scholarly journals STORM SURGE FORECASTING METHODS IN ENCLOSED SEAS

1978 ◽  
Vol 1 (16) ◽  
pp. 58
Author(s):  
P.F. Hamblin
Keyword(s):  
Water Level ◽  
Storm Surge ◽  
Storm Surges ◽  
High Water ◽  
Computational Effort ◽  
Water Levels ◽  
Large Lakes ◽  
Forecasting Methods ◽  
The Stability ◽  
Storm Surge Forecasting

Storm surges in enclosed seas although generally not as large in amplitude as their oceanic counterparts are nonetheless of considerable importance when low lying shoreline profiles, shallow water depth, and favourable geographical orientation to storm winds occur together. High water may result in shoreline innundation and in enhanced shoreline erosion. Conversely low water levels are hazardous to navigation. The purpose of this paper is to discuss the problem of storm surge forecasting in enclosed basins with emphasis on automated operational procedures. In general, operational forecasting methods must be based on standard forecast parameters, require a minimum of computational effort in the preparation of the forecast, must be applicable to lakes of different geometry and to any point on the shore, and to be able to resolve water level changes on an hourly basis to 10 cm in the case of high water level excursions associated with large lakes and less than that for smaller lakes. Particular physical effects arising in lakes which make these constraints difficult to fulfill are the reflections of resurgences of water levels arising from lateral boundaries, the stability of the atmospheric boundary layer and the presence of such subsynoptic disturbances as squall lines and travelling pressure jumps.

scholarly journals Assimilating SAR-derived water level data into a hydraulic model: a case study

2011 ◽  
Vol 8 (1) ◽  
pp. 2103-2144 ◽  
Author(s):  
L. Giustarini ◽  
P. Matgen ◽  
R. Hostache ◽  
M. Montanari ◽  
D. Plaza ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Cross Sections ◽  
Hydraulic Model ◽  
Water Levels ◽  
Hydraulic Models ◽  
Level Data ◽  
Water Level Data ◽  
Assimilation Scheme

Abstract. Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction to the model forecast uncertainty. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.

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