Prediction Of Water Table Depth Research Articles

DRAINMOD simulation of drain spacing impact on canola yield in heavy clay soils in the Canadian prairies

AbstractExcess moisture within the root zone due to the shallow water table is a leading cause of crop loss in Manitoba. In this study, the ability of the DRAINMOD model to predict water table depth (WTD) in clayey soil was evaluated using measured field data from the 2019 and 2020 canola‐growing seasons in Arborg, Manitoba, Canada. Statistical analysis and graphical plots showed close agreement between the measured and simulated WTD with an overall coefficient of determination (R2), root mean square error (RMSE), mean average error (MAE) and mean bias error (MBE) of 0.93, 9.84 cm, 7.06 cm and −0.13 cm, respectively. Since the model simulation was deemed satisfactory, the model was run with 30‐year historical climate data to assess the impacts of different drain spacing on canola yield. Simulation results showed that the average surface runoff increased while average drainage and relative canola yield decreased as drain spacing increased. The simulation results suggest that long‐term average yield would be maximized by close drain spacing ≤ 15 m. Economic analysis showed that 10 m drain spacing would maximize the return on investment. The need for long‐term simulations to develop appropriate site‐specific water management strategies is demonstrated.

Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.

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

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.

DRAINMOD modeling framework for simulating controlled drainage effect on lateral seepage from artificially drained fields

We demonstrated a DRAINMOD modeling framework to predict controlled drainage (CD) effect on the fate of water in artificially drained agricultural fields, which is key for determining the water quality benefits of the practice. To demonstrate this modeling framework, DRAINMOD simulated the hydrology of a subsurface drained grass field in Eastern North Carolina, U.S. under both free drainage (FD) and CD. Three scenarios were simulated for each water management: no lateral seepage (LS) and LS with constant and dynamic hydraulic head (Hr). For each scenario, predicted water table depth (WTD) and subsurface drainage were compared to observed values using Mean Absolute Error, Nash-Sutcliffe Efficiency, and Normalized Percent Error. Predicted water balance components for different scenarios were also investigated. Results clearly showed that LS was a significant component of the water balance for CD. Model predictions showed that 96% of the reduction in subsurface drainage due to CD could be attributed to LS (33.5 cm yr−1). The large values of LS predicted by the model were attributed to the presence of a permeable sandy layer in the soil profile, the shallow management depth of the drain outlet, and the small size of the experimental field plots. Agreement between predicted and observed WTD and subsurface drainage ranged from acceptable to excellent for FD with and without considering LS. In contrast, DRAINMOD simulations for CD yielded acceptable predictions only for the scenario considering LS with dynamic Hr. This study demonstrated the power of process-based simulation models, such as DRAINMOD, for interpreting and explaining data of experimental studies and underscored the importance of using a proper model calibration strategy for yielding reliable predictions. This study highlights the need for well-coordinated experimental and modeling research to further investigate how seepage affect CD performance for reducing drainage flow and nitrogen losses from artificially drained agricultural fields.

Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

Integrated groundwater management is a major challenge for industrial, agricultural and domestic activities. In some agricultural production systems, optimized water table management represents a significant factor to improve crop yields and water use. Therefore, predicting water table depth (WTD) becomes an important means to enable real-time planning and management of groundwater resources. This study proposes a decision-tree-based modelling approach for WTD forecasting as a function of precipitation, previous WTD values and evapotranspiration with applications in groundwater resources management for cranberry farming. Firstly, two decision-tree-based models, namely Random Forest (RF) and Extreme Gradient Boosting (XGB), were parameterized and compared to predict the WTD up to 48 -h ahead for a cranberry farm located in Québec, Canada. Secondly, the importance of the predictor variables was analyzed to determine their influence on WTD simulation results. WTD measurements at three observation wells within a cranberry field, for the growing period from July 8, 2017 to August 30, 2017, were used for training and testing the models. Statistical parameters such as the mean squared error, coefficient of determination and Nash-Sutcliffe Efficiency coefficient were used to measure models performance. The results show that the XGB model outperformed the RF model for all predictions of WTD and was, accordingly, selected as the optimal model. Among the predictor variables, the antecedent WTD was the most important for water table depth simulation, followed by the precipitation. Based on the most important variables and optimal model, the prediction error for entire WTD range was within ±5 cm for 1-, 12-, 24-, 36- and 48 -h predictions. The XGB models can provide useful information on the WTD dynamics and a rigorous simulation for irrigation planning and management in cranberry fields.

Calibration of DRAINMOD for prediction of water table depths and drain discharges under waterlogged Vertisols of Maharashtra, India

An estimation of optimal design parameters of subsurface drainage system through monitoring of water table depths and drain discharges are expensive in terms of time and money. The simulation modeling is an effective tool for estimation of drainage design parameters at less cost and short time. In view to this, calibration of DRAINMOD model for prediction of water table depths and drain discharges were conducted by installing subsurface drainage system with 40 m drain spacing and 1.0 m drain depth at Agricultural Research Station, Kasbe Digraj, Dist. Sangli (Maharashtra) during 2012-13 to 2013-14. The field data on water table depth and drain discharge were used for calibration of DRAINMOD model. The input data files on climatic, soil, crop and drainage design system parameters were attached to DRAINMOD model and calibrated successfully. It is found that both observed and simulated water table depths and drain discharges showed a fluctuating trend and predicted both water table depths and drain discharges closely with the observed values during frequent rainy days and following the rainy days. The DRAINMOD model reliably predicted water table depths with a goodness of fit (R2 = 0.97), MAE (12.23 cm), RMSE (15.49 cm) and CRM (0.05); drain discharges with R2 of 0.93, MAE of 0.095 mm day-1, RMSE of 0.1876 mm day-1and CRM of 0.04. Thus, the calibrated DRAINMOD model can be used to simulate the water table depths and drain discharges in semi-arid climatic conditions of Maharashtra and in turn to estimate and evaluate drain spacing and depth.

A geostatistical approach for multi-source data fusion to predict water table depth

Accurate water table depth mapping is important for water management and activity planning. The joint use of exhausted geospatial raster data with sparse field measurements could improve predictions. The aim of this work was to fuse different support data, collected with remote sensors, with point soil field observations to improve water table depth prediction. A method for multi-source data fusion is described in detail, based on multivariate geostatistics and exemplified with a case study in a conservation area of 5700 ha in the state of São Paulo, Brazil. TanDEM-X digital surface model with 90 m resolution and SAFER (Simple Algorithm for Evapotranspiration Retrieving) data calculated from Sentinel-2 images with 20 m resolution, were jointly used with water table depth and soil physical variables measured at 56 locations to predict water table depth in two hydrological years (2015–16 and 2016–17). Data were transformed to normal distributions using the Gaussian anamorphosis approach. A Linear Model of Coregionalization (LMC), calculated for all direct and cross-variograms of the eleven variables of study, was regularized at block support for multi-collocated block cokriging predictions. Support change correction was made to reduce punctual variance to block variances. Univariate and multivariate geostatistical interpolation methods were compared through cross validation. The uncertainty associated to the water table depths estimated by multivariate approach was lower than those by the univariate approach. Moreover, multivariate predictions incorporated the influences induced by local relief, vegetation and soil properties. Confidence interval maps, presented as uncertainty measure, reveal areas with higher and lower precision of groundwater level prediction that could be effectively used as support in land use management.

Machine-Learning Methods for Water Table Depth Prediction in Seasonal Freezing-Thawing Areas.

Long-term and accurate predictions of regional groundwater hydrology are important for maintaining environmental sustainability in arid agricultural areas that experience seasonal freezing and thawing where serious water-saving measurements are used. In this study, we firstly developed a machine-learning method by integrating a multivariate time series controlled auto-regressive method and the ridge regression method (CAR-RR) for water table depth modeling. We applied and evaluated this model in the Hetao Irrigation District, located in northwest China where the freezing-thawing period is 5 months long. To train and validate the model, we used monthly data of water diversion, precipitation, evaporation, and drainage from 1995 to 2013. The CAR-RR model yielded more accurate results than the support vector regression (SVR) and multiple linear regression (MLR) models did in the validation period. To extend the model applicability during freezing-thawing periods, we included additional temperature information. We compared results obtained using temperature only during the freezing-thawing period with results obtained without temperature, which showed that the input data of the temperature during the freezing-thawing period significantly improved the model accuracy. To resolve the problem of capturing the peaks and troughs of CAR-RR, we further developed an integrated CAR-SVR model to consider the nonlinearity. The optimal model (CAR-SVR) was then used to predict the water table depth under future water-saving measurements. It demonstrated that water diversion was the most important factor affecting the water table depth. A water table depth with less than 3.64 billion m3 water diversion will result in risks of environment problems.

Assessment of groundwater utilization status and prediction of water table depth using different heuristic models in an Indian interbasin

The knowledge of fluctuation of water table depth is highly required for proper planning and sustainable development of available water resources. This study intends to study groundwater behaviour under changing scenario in the lower part of Ganga–Ramganga interbasin. It also investigates the comparative performance of soft computing techniques, i.e. co-active neuro-fuzzy inference system (CANFIS), fuzzy logic and radial basis function network (RBFN), which are used for prediction of water table depth in the study area. Components of groundwater recharge and discharge along with seasonal water table depth covering a period of 23 years (1990–2012) are used to develop four combination sets of input variables. Different CANFIS structures, fuzzy logic rules and RBFN structures are applied to these combinations of input variables, and the best combinations are selected on the basis of the values of different performance indicators such as coefficient of determination (R2), mean absolute deviation, root mean square error, coefficient of variation of error residuals, Nash–Sutcliff efficiency, correlation coefficient (r), absolute prediction error and performance index. The result of this study indicates the superiority of fuzzy logic rule-based model than of CANFIS models and RBFN model in predicting water table depth.

Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0

Abstract. Improving global-scale model representations of near-surface soil moisture and groundwater hydrology is important for accurately simulating terrestrial processes and predicting climate change effects on water resources. Most existing land surface models, including the default E3SM Land Model (ELMv0), which we modify here, routinely employ different formulations for water transport in the vadose and phreatic zones. Clark et al. (2015) identified a variably saturated Richards equation flow model as an important capability for improving simulation of coupled soil moisture and shallow groundwater dynamics. In this work, we developed the Variably Saturated Flow Model (VSFM) in ELMv1 to unify the treatment of soil hydrologic processes in the unsaturated and saturated zones. VSFM was tested on three benchmark problems and results were evaluated against observations and an existing benchmark model (PFLOTRAN). The ELMv1-VSFM's subsurface drainage parameter, fd, was calibrated to match an observationally constrained and spatially explicit global water table depth (WTD) product. Optimal spatially explicit fd values were obtained for 79 % of global 1.9∘ × 2.5∘ grid cells, while the remaining 21 % of global grid cells had predicted WTD deeper than the observationally constrained estimate. Comparison with predictions using the default fd value demonstrated that calibration significantly improved predictions, primarily by allowing much deeper WTDs. Model evaluation using the International Land Model Benchmarking package (ILAMB) showed that improvements in WTD predictions did not degrade model skill for any other metrics. We evaluated the computational performance of the VSFM model and found that the model is about 30 % more expensive than the default ELMv0 with an optimal processor layout. The modular software design of VSFM not only provides flexibility to configure the model for a range of problem setups but also allows for building the model independently of the ELM code, thus enabling straightforward testing of the model's physics against other models.

Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas

Predicting water table depth over the long-term in agricultural areas presents great challenges because these areas have complex and heterogeneous hydrogeological characteristics, boundary conditions, and human activities; also, nonlinear interactions occur among these factors. Therefore, a new time series model based on Long Short-Term Memory (LSTM), was developed in this study as an alternative to computationally expensive physical models. The proposed model is composed of an LSTM layer with another fully connected layer on top of it, with a dropout method applied in the first LSTM layer. In this study, the proposed model was applied and evaluated in five sub-areas of Hetao Irrigation District in arid northwestern China using data of 14 years (2000–2013). The proposed model uses monthly water diversion, evaporation, precipitation, temperature, and time as input data to predict water table depth. A simple but effective standardization method was employed to pre-process data to ensure data on the same scale. 14 years of data are separated into two sets: training set (2000–2011) and validation set (2012–2013) in the experiment. As expected, the proposed model achieves higher R2 scores (0.789–0.952) in water table depth prediction, when compared with the results of traditional feed-forward neural network (FFNN), which only reaches relatively low R2 scores (0.004–0.495), proving that the proposed model can preserve and learn previous information well. Furthermore, the validity of the dropout method and the proposed model’s architecture are discussed. Through experimentation, the results show that the dropout method can prevent overfitting significantly. In addition, comparisons between the R2 scores of the proposed model and Double-LSTM model (R2 scores range from 0.170 to 0.864), further prove that the proposed model’s architecture is reasonable and can contribute to a strong learning ability on time series data. Thus, one can conclude that the proposed model can serve as an alternative approach predicting water table depth, especially in areas where hydrogeological data are difficult to obtain.

Modeling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network

The investigation of the dynamic response of aquifer to rainfall with variation of the atmospheric conditions is a key issue for groundwater resource management. A data-driven dynamic recurrent neural network approach, based on multi-objective optimization is used here to forecast groundwater levels as a function of rainfall, temperature, humidity, runoff and evapotranspiration data. Recurrent Neural Network (RNN) with variable transfer functions like tangential sigmoidal, purelin and logarithmic sigmoidal are used to compute the relative performance of the model. The results demonstrate the transition behavior due to changing evapotranspiration and runoff. Statistical and graphical indicators are used to compare the results. The statistical indicators used in this work are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). From the results, evapotranspiration loss and run off are the influencing parameters which affect the depth to water table in the ground water reservoir. It is observed that calculated losses due to evapotranspiration are comparatively less during high precipitation in the Rengali province. Results of R2 suggest that inclusion of evapotranspiration and runoff in different scenario improves the model efficiency in predicting water table depth.

Modeling water table depth using adaptive Neuro-Fuzzy Inference System

A comparative study on prediction of water table depth using Back Propagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is addressed in this paper. BPNN and ANFIS models are developed using precipitation, temperature, humidity, surface runoff and evapotranspiration loss to simulate water table depth fluctuations at Rengali, Odisha. Multiple linear regressions have been applied to observe the correlation between the dependent variable i.e. water table depth and the independent variables i.e. precipitation, temperature, humidity, surface runoff and evapotranspiration losses. Six different types of membership functions for ANFIS and three different transfer functions in case of BPNN are employed to compare the results of the models to predict water table depth. The model considering the physical parameters- precipitation, average temperature, evapotranspiration losses and humidity shows better performances on ANFIS as compared to BPNN. BPNN model using purelin transfer function produced best result with MAE testing 0.189701, RMSE testing 0.209842, and R2 testing 0.8512. ANFIS model with gauss2 membership function as input mf presented a superior ability to predict water table depth fluctuation over BPNN with MAE testing 0.080684, RMSE testing 0.089129, and R2 testing 0.9671. Taken as a whole, ANFIS is superior to BPNN for predicting water table depth.

Prediction of Groundwater Dynamics for Sustainable Water Resource Management in Bogra District, Northwest Bangladesh

The green revolution in the northwest region of Bangladesh over the past three decades has based on groundwater irrigation. For sustainable agricultural accretion, groundwater dynamics play a vital role in this region. In this study, the groundwater level dynamics have been analyzed with a model named “MAKESENS” and with geographical information systems (GIS). The study indicates that, in most of the wells, the water table (WT) depth and the rainfall intensity are declining slowly. The prediction of WT depth during the period of 2020, 2040, and 2060 indicate that, in some cases, the WT depth will approximately double by the year 2060, considering the present declining trend. This result suggests that, for the sustainable management of groundwater, necessary measures should be adopted to avoid or reduce the severe ecological, social, and economic impacts of groundwater mining. Moreover, crop diversification, conservation techniques, increasing irrigation efficiency, rainwater harvesting, etc. can be adopted to avoid groundwater declination and consequently to enhance the sustainable use of groundwater resources in the area.

A stochastic simulation model to early predict susceptible areas to water table level fluctuations in North Sinai, Egypt

Water table depth (WTD) is an important map layer for many environmental models’ assessments. To develop an early water table prediction model for North Sinai, Egypt, four approaches were considered in this study: GIS, remote sensing, simulation and stochastic methods. Stochastic (using time-series) modeling enables us to characterize the water table dynamics in terms of risk, and it allows model uncertainty to be taken into account without the complexity of physical mechanistic models. The results indicate that, time-series modeling is an effective method to characterize the seasonal patterns of WTDs in the area. The model performs very well using Nash and Sutcliffe coefficient of efficiency (NSE) which indicates a fit of the model to the data. Sequential Gaussian Simulation was explored as a way to model water table spatial variability. A 95% probability level was calculated as a measure for risk of shallow water tables. The limits established for risks of shallow WTDs at April 1st were 0.5m below the ground surface. A map showing the risk that the WTD at April 1st and October 1st in a future year will be shallower than 0.5m was presented. If the risk is close to 50%, it will be difficult to take a decision in water management or land use planning. The results found a negligible risk of shallow water tables for this date. The level of WTD will be about 0.30–0.45m higher than the present level in the next 20years. There is a better agreement between the simulated and measured data (8cm difference) which may be associated with an overestimation of the evaporative losses from the surface. Finally, Landsat image was not useful for WTD prediction; however, it may be useful for soil moisture prediction.

Evaluation of DRAINMOD for Potato Crop under Cold Conditions in the Canadian Prairies

<abstract> <b><sc>Abstract.</sc></b> Subsurface drainage is used for removing excess water in poorly drained agricultural lands. The performance of subsurface drainage systems varies depending on the soil, crop, and climatic factors. Field evaluation of drainage systems under different field conditions can be costly and time-consuming, necessitating the use of computer models for the design and the evaluation of long-term performance. DRAINMOD is a widely used model for simulating shallow groundwater hydrology in poorly or artificially drained farmlands. The objective of this study was to evaluate DRAINMOD for simulating the hydrology and potato (Solanum tuberosum) crop yield under cold climatic conditions. Data were obtained from a field study that was conducted during 2010 to 2012 in southern Manitoba in a fine sandy loam soil. DRAINMOD calibration and validation were done using data from subsurface-drained plots with drain spacing of 15 m and average drain depth of 1.06 m. Daily subsurface drainage flow, weather parameters, irrigation rate, and water table depth were measured onsite. Subsurface drainage and water table depth were the main variables that were evaluated with DRAINMOD simulations. In general, DRAINMOD predictions of subsurface drainage flow and water table depth were in good agreement with observed values on a daily basis. Statistical parameters showed that DRAINMOD predictions of daily water table depth were more accurate than model predictions of daily drainage flow. The Nash-Sutcliffe modeling efficiency values were in the range of 0.69 to 0.76 for drainage flow and 0.57 to 0.94 for water table depth. The mean absolute error was between 70 and 134 mm for water table depth predictions and between 0.42 and 0.44 mm for drainage flow predictions. The root mean square error was 0.77 to 1.14 mm for drainage flow and 83 to 147 mm for water table depth. DRAINMOD overpredicted potato yield by up to 1%. Based on the results, it is concluded that DRAINMOD predictions of the shallow groundwater hydrology under cold climate were satisfactory.

Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth

Accurate and reliable interpolation of groundwater depth over a region is a pre-requisite for efficient planning and management of water resources. The performance of two deterministic, such as inverse distance weighting (IDW) and radial basis function (RBF) and two stochastic, i.e., ordinary kriging (OK) and universal kriging (UK) interpolation methods was compared to predict spatio-temporal variation of groundwater depth. Pre- and post-monsoon groundwater level data for the year 2006 from 110 different locations over Delhi were used. Analyses revealed that OK and UK methods outperformed the IDW method, and UK performed better than OK. RBF also performed better than IDW and OK. IDW and RBF methods slightly underestimated and both the kriging methods slightly overestimated the prediction of water table depth. OK, RBF and UK yielded 27.52, 27.66 and 51.11 % lower RMSE, 27.49, 35.34 and 51.28 % lower MRE, and 14.21, 16.12 and 21.36 % higher R2 over IDW. The isodepth-area curves indicated the possibility of exploitation of groundwater up to a depth of 20 m.

Predicting Shallow Water Table Depth at Regional Scale: Optimizing Monitoring Network in Space and Time

Shallow water table levels can be predicted using several approaches, either based on climatic records, on field evidences based on soil morphology, or on the outputs of physically based models. In this study, data from a monitoring network in a relevant agricultural area of Northern Italy (ca. 12,000 Km2) were used to develop a data driven model for predicting water table depth in space and time from meteorological data and long-term water table characteristics and to optimize sampling density in space and time. Evolutionary Polynomial Regressions (EPR) were used to calibrate a predictive tool based on climatic data and on the records from 48 selected sites (N = 5,611). The model was validated against the water table depths observed in 15 independent sites (N = 1,739), resulting in a mean absolute error of 30.8 cm (R2 = 0.61). The model was applied to the whole study area, using the geostatistical estimates of the average water table depth as input, to provide spatio-temporal maps of the water table depth. The impact of the degradation of data input in the temporal and spatial domain was then assessed following two approaches. In the first case, three different EPR models were calibrated based on 25 %, 50 % and 75 % of the available data, and the error indexes compared. In the second case, an increasing number of monitoring sites were removed from the initial data set, and the associated increased kriging standard deviation was assessed. Reducing the average sampling frequency from 1.5 per month to 1 every 40 days did not impact significantly on the prediction capability of the proposed model. Reducing the sampling frequency to 1 every 4 months resulted in a loss of accuracy <3 %, while removing more than half locations from the network, resulted in a global loss of information <15 %.

Ecology of testate amoebae in peatlands of central China and development of a transfer function for paleohydrological reconstruction

Testate amoebae are a diverse and abundant group of protozoa that constitute a large proportion of biomass in many ecosystems and probably fill important roles in ecosystem function. These microorganisms have attracted the interest of paleoecologists because the preserved shells of testate amoebae and their known hydrological preferences enable reconstruction of past hydrological change. In ombrotrophic peatlands, surface wetness reflects hydroclimate, so testate amoebae can play an important role in reconstruction of Holocene climate change. Previous studies, however, have been geographically restricted, mostly to North America and Europe. We studied the ecology of testate amoebae in peatlands from central China in relation to hydrology, pH and metal concentrations. We found that testate amoeba community structure was correlated with depth to water table (DWT) and that the hydrological preferences of species generally matched those of previous studies. We developed a weighted average DWT transfer function that enables prediction of water table depth with a cross-validated mean error of <5 cm. Our results demonstrate the potential for using testate amoebae to reconstruct paleohydrology in China. Such studies could contribute to our understanding of Holocene climate changes in China, particularly regarding past Asian monsoon activity.

Utilizing artificial neural network for forecasting groundwater table depths fluctuations

This study presented the model of predicting the water table fluctuation in flood plain of Sepidroud watershed (North of Iran-Gilan). The model for prediction of water table depth was developed leaning on artificial neural network. The neural network with different numbers of hidden layer neurons was developed by using 4 years (2004-2007) monthly rainfall, potential evapotranspiration and influencing wells as input and water table depth as output. The best model was selected based on mean square error. The results showed that artificial neural network could be used to predict water table depth in aquifer with good convergence and maximum error was 5% approximately.