scholarly journals Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland

2021 ◽  
Vol 13 (10) ◽  
pp. 5474
Author(s):  
Sebastian Gutierrez Pacheco ◽  
Robert Lagacé ◽  
Sandrine Hugron ◽  
Stéphane Godbout ◽  
Line Rochefort
Keyword(s):  
Water Table ◽  
Linear Method ◽  
Estimation Method ◽  
Water Table Depth ◽  
Support Vector ◽  
Eastern Canada ◽  
Ombrotrophic Peatland ◽  
Daily Data ◽  
Daily Water ◽  
Potential Methods

Daily measurements of the water table depth are sometimes needed to evaluate the influence of seasonal water stress on Sphagnum recolonization in restored ombrotrophic peatlands. However, continuous water table measurements are often scarce due to high costs and, as a result, water table depth is more commonly measured manually bimonthly with daily logs in few reference wells. A literature review identified six potential methods to estimate daily water table depth with bimonthly records and daily measurements from a reference well. A new estimation method based on the time series decomposition (TSD) is also presented. TSD and the six identified methods were compared with the water table records of an experimental peatland site with controlled water table regime located in Eastern Canada. The TSD method was the best performing method (R2 = 0.95, RMSE = 2.48 cm and the lowest AIC), followed by the general linear method (R2 = 0.92, RMSE = 3.10 cm) and support vector machines method (R2 = 0.91, RMSE = 3.24 cm). To estimate daily values, the TSD method, like the six traditional methods, requires daily data from a reference well. However, the TSD method does not require training nor parameter estimation. For the TSD method, changing the measurement frequency to weekly measurements decreases the RMSE by 16% (2.08 cm); monthly measurements increase the RMSE by 13% (2.80 cm).

Comparison of Daily Water Table Depth Prediction by Four Simulation Models

1996 ◽  
Vol 39 (1) ◽  
pp. 111-118 ◽  
Author(s):  
E. D. Desmond ◽  
A. D. Ward ◽  
N. R. Fausey ◽  
S. R. Workman
Keyword(s):  
Water Table ◽  
Simulation Models ◽  
Water Table Depth ◽  
Depth Prediction ◽  
Daily Water

scholarly journals Bi-criteria evaluation of the MIKE SHE model for a forested watershed on the South Carolina coastal plain

2010 ◽  
Vol 14 (6) ◽  
pp. 1033-1046 ◽  
Author(s):  
Z. Dai ◽  
C. Li ◽  
C. Trettin ◽  
G. Sun ◽  
D. Amatya ◽  
...  
Keyword(s):  
South Carolina ◽  
Water Table ◽  
Coastal Plain ◽  
Model Calibration ◽  
Water Table Depth ◽  
Forested Watershed ◽  
Mike She ◽  
Monthly Streamflow ◽  
Daily Water ◽  
Model Calibration And Validation

Abstract. Hydrological models are important tools for effective management, conservation and restoration of forested wetlands. The objective of this study was to test a distributed hydrological model, MIKE SHE, by using bi-criteria (i.e., two measurable variables, streamflow and water table depth) to describe the hydrological processes in a forested watershed that is characteristic of the lower Atlantic Coastal Plain. Simulations were compared against observations of both streamflow and water table depth measured on a first-order watershed (WS80) on the Santee Experimental Forest in South Carolina, USA. Model performance was evaluated using coefficient of determination (R2) and Nash-Sutcliffe's model efficiency (E). The E and root mean squared error (RMSE) were chosen as objective functions for sensitivity analysis of parameters. The model calibration and validation results demonstrated that the streamflow and water table depth were sensitive to most of the model input parameters, especially to surface detention storage, drainage depth, soil hydraulic properties, plant rooting depth, and surface roughness. Furthermore, the bi-criteria approach used for distributed model calibration and validation was shown to be better than the single-criterion in obtaining optimum model input parameters, especially for those parameters that were only sensitive to some specific conditions. Model calibration using the bi-criteria approach should be advantageous for constructing the uncertainty bounds of model inputs to simulate the hydrology for this type of forested watersheds. R2 varied from 0.60–0.99 for daily and monthly streamflow, and from 0.52–0.91 for daily water table depth. E changed from 0.53–0.96 for calibration and 0.51–0.98 for validation of daily and monthly streamflow, while E varied from 0.50–0.90 for calibration and 0.66–0.80 for validation of daily water table depth. This study showed that MIKE SHE could be a good candidate for simulating streamflow and water table depth in coastal plain watersheds.

scholarly journals Bi-criteria evaluation of MIKE SHE model for a forested watershed on South Carolina coastal plain

2010 ◽  
Vol 7 (1) ◽  
pp. 179-219 ◽  
Author(s):  
Z. Dai ◽  
C. Li ◽  
C. Trettin ◽  
G. Sun ◽  
D. Amatya ◽  
...  
Keyword(s):  
South Carolina ◽  
Water Table ◽  
Coastal Plain ◽  
Model Calibration ◽  
Water Table Depth ◽  
Forested Watershed ◽  
Mike She ◽  
Monthly Streamflow ◽  
Daily Water ◽  
Model Calibration And Validation

Abstract. Hydrological models are important tools for effective management, conservation and restoration of forested wetlands. The objective of this study was to test a distributed hydrological model, MIKE SHE by using bi-criteria (two measurable variables, streamflow and water table depth) to describe the hydrological processes in a forested watershed that is characteristic of the lower Atlantic Coastal Plain. Simulations were compared against observations of both streamflow and water table depth measured on a first-order watershed (WS80) on the Santee Experimental Forest in South Carolina, USA. Model performance was evaluated using coefficient of determination (R2) and Nash-Sutcliffe's model efficiency (E). The E and root mean squared error (RMSE) were chosen as objective functions for sensitivity analysis of parameters. The model calibration and validation results demonstrated that the streamflow and water table depth were sensitive to most of the model input parameters, especially to surface detention storage, drainage depth, soil hydraulic properties, plant rooting depth, and surface roughness. Furthermore, the bi-criteria used for distributed model calibration and validation was shown to be better than the single-criterion in obtaining optimum model input parameters, especially for those parameters that were only sensitive to some specific conditions. Model calibration using the bi-criteria approach should be advantageous for constructing the uncertainty bounds of model inputs to simulate the hydrology for this type of forested watersheds. R2 varied from 0.60–0.99 for daily and monthly streamflow, and from 0.52–0.91 for daily water table depth. E changed from 0.53–0.96 for calibration and 0.51–0.98 for validation of daily and monthly streamflow, while E varied from 0.50–0.90 for calibration and 0.66–0.80 for validation of daily water table depth. This study showed that MIKE SHE was applicable for predicting the streamflow and water table depth in this coastal plain watershed.

A Daily Water Table Depth Computing Model for Poorly Drained Soils

Wetlands ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Devendra M. Amatya ◽  
Marcin Fialkowski ◽  
Agnieszka Bitner
Keyword(s):  
Water Table ◽  
Water Table Depth ◽  
Computing Model ◽  
Daily Water ◽  
Poorly Drained Soils

scholarly journals Water table depth prediction based on Deep learning models in Electrical Power Transmission Lines area

2021 ◽  
Author(s):  
Shijun Wang ◽  
Chang Ping ◽  
Ning Wang ◽  
Jing Wen ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Water Table ◽  
Transmission Lines ◽  
Power Transmission ◽  
Model Simulation ◽  
Electrical Power ◽  
Water Table Depth ◽  
Weather Data ◽  
Power Transmission Lines ◽  
Data Simulation ◽  
Daily Data

Background: Predicting water table depth in Electrical Power Transmission Lines area presents great importance and helps the decision makers do the safety analysis during the project. The present study predicts the water table depth with observed weather data and hydrologic data. Method: The study first compared the results of LSTM, GRU, LSTM-S2S, and FFNN models in daily data simulation. Moreover, two scenarios (S1 and S2) were set to identify the effect of the water component on water table depth simulation. In addition, in order to analyze how data time scale influences the model simulation results, the monthly scale data was simulated by LSTM, GRU, and LSTM-S2S models. Result: The result indicated that LSTM-S2S was the best model for predicting daily water table depth among the four models. By contrast, FFNN performed the worst. LSTM and GRU model performed equally well both in daily data and monthly data simulation. S1 performed better than S2 in the water table depth simulation. The average daily performance of R2 and NSE was both higher than that in the monthly results with LSTM, GRU, and LSTM-S2S models. Conclusion: As a result, the method in the present study can be used to simulate the water table depth in the future in Electrical Power Transmission Lines area.

scholarly journals Temporal and Local Heterogeneities of Water Table Depth under Different Agricultural Water Management Conditions

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2148
Author(s):  
Jonathan A. Lafond ◽  
Silvio J. Gumiere ◽  
Virginie Vanlandeghem ◽  
Jacques Gallichand ◽  
Alain N. Rousseau ◽  
...  
Keyword(s):  
Water Management ◽  
Water Table ◽  
Cropping Systems ◽  
Irrigation Management ◽  
Sprinkler Irrigation ◽  
Water Table Depth ◽  
Management Systems ◽  
Integrated Water Management ◽  
Growing Seasons ◽  
Good Drainage

Integrated water management has become a priority for cropping systems where subirrigation is possible. Compared to conventional sprinkler irrigation, the controlling water table can lead to a substantial increase in yield and water use efficiency with less pumping energy requirements. Knowing the spatiotemporal distribution of water table depth (WTD) and soil properties should help perform intelligent, integrated water management. Observation wells were installed in cranberry fields with different water management systems: Bottom, with good drainage and controlled WTD management; Surface, with good drainage and sprinkler irrigation management; Natural, without drainage, or with imperfectly drained and conventional sprinkler irrigation. During the 2017–2020 growing seasons, WTD was monitored on an hourly basis, while precipitation was measured at each site. Multi-frequential periodogram analysis revealed a dominant periodic component of 40 days each year in WTD fluctuations for the Bottom and Surface systems; for the Natural system, periodicity was heterogeneous and ranged from 2 to 6 weeks. Temporal cross correlations with precipitation show that for almost all the sites, there is a 3 to 9 h lag before WTD rises; one exception is a subirrigation site. These results indicate that automatic water table management based on continuously updated knowledge could contribute to integrated water management systems, by using precipitation-based models to predict WTD.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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