scholarly journals Validating a spatially distributed hydrological model with soil morphology data

2014 ◽  
Vol 18 (9) ◽  
pp. 3481-3498 ◽  
Author(s):  
T. Doppler ◽  
M. Honti ◽  
U. Zihlmann ◽  
P. Weisskopf ◽  
C. Stamm
Keyword(s):  
Groundwater Level ◽  
Morphological Data ◽  
Distributed Model ◽  
Management Decisions ◽  
Data Set ◽  
Detailed Model ◽  
Groundwater Levels ◽  
Artificial Drainage ◽  
Spatial Coverage ◽  
Spatially Distributed

Abstract. Spatially distributed models are popular tools in hydrology claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for inputs of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography and artificial drainage. We translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to observed groundwater levels and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the the groundwater level predictions were not accurate enough to be used for the prediction of saturated areas. Groundwater level dynamics were not adequately reproduced and the predicted spatial saturation patterns did not correspond to those estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a model that better represents processes at the boundary between the unsaturated and the saturated zone. However, data needed for such a more detailed model are not generally available. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

scholarly journals Validating a spatially distributed hydrological model with soil morphology data

2013 ◽  
Vol 10 (10) ◽  
pp. 12905-12950
Author(s):  
T. Doppler ◽  
M. Honti ◽  
U. Zihlmann ◽  
P. Weisskopf ◽  
C. Stamm
Keyword(s):  
Spatial Resolution ◽  
Spatial Patterns ◽  
Groundwater Level ◽  
High Spatial Resolution ◽  
Complex Model ◽  
Management Decisions ◽  
Groundwater Levels ◽  
Calibration And Validation ◽  
Soil Saturation ◽  
Spatially Distributed

Abstract. Spatially distributed hydrological models are popular tools in hydrology and they are claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time-series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for the transport of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography. Around 40% of the catchment area are artificially drained. We measured weather data, discharge and groundwater levels in 11 piezometers for 1.5 yr. For broadening the spatially distributed data sets that can be used for model calibration and validation, we translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. We used redox-morphology signs for these estimates. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to the groundwater levels in the piezometers and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the accuracy of the groundwater level predictions was not high enough to be used for the prediction of saturated areas. The groundwater level dynamics were not adequately reproduced and the predicted spatial patterns of soil saturation did not correspond to the patterns estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a more complex model. Especially high spatial resolution and very detailed process representations at the boundary between the unsaturated and the saturated zone are expected to be crucial. The data needed for such a detailed model are not generally available. The high computational demand and the complex model setup would require more resources than the direct identification of saturated areas in the field. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

scholarly journals Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

2017 ◽  
Vol 32 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Basant Yadav ◽  
Sudheer Ch ◽  
Shashi Mathur ◽  
Jan Adamowski
Keyword(s):  
Soft Computing ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Support Vector ◽  
Complex Nature ◽  
Data Set ◽  
Groundwater Levels ◽  
Soft Computing Techniques ◽  
Learning Machine ◽  
The Comparative Study

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

scholarly journals Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX

2020 ◽  
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Level ◽  
Short Term Memory ◽  
State Of The Art ◽  
Predictive Ability ◽  
Data Set ◽  
Groundwater Levels ◽  
Narx Models ◽  
Artificial Neural

Abstract. It is now well established to use shallow artificial neural networks (ANN) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, especially shallow recurrent networks frequently seem to be excluded from the study design despite the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANN namely nonlinear autoregressive networks with exogenous inputs (NARX), and popular state-of-the-art DL-techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN). We compare both the performance on sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period, while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. We observe that for seq2val forecasts NARX models on average perform best, however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL-models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL-techniques; however, LSTMs and CNNs might perform substantially better with a larger data set, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

scholarly journals Hydraulic-Hydrology Analysis of the Turbulent Seepage Flow within Karst Aquifer of the Golubinka Spring Catchment

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Goran Lončar ◽  
Željko Šreng ◽  
Damir Bekić ◽  
Duška Kunštek
Keyword(s):  
Groundwater Level ◽  
Sea Water ◽  
Karst Aquifer ◽  
Seepage Flow ◽  
Spring Discharge ◽  
Data Set ◽  
Groundwater Levels ◽  
Matrix Flow ◽  
Spring Catchment ◽  
The Difference

This paper shows the results of the hydraulic-hydrologic calculations of karst spring discharges and the groundwater level in the aquifer of spring catchment. The calculations were performed for the Golubinka spring in Zadar area for the 4-year period. The chosen approach was a model using relatively scarce data set, including limnigraphic data on the difference between the sea water level and the freshwater level on the spring itself and the precipitation data from the meteorological station Zadar. The determination of discharge hydrographs, based on inherent assumptions and available data, yields the proportionality coefficients between the discharge and the limnigraphic data on the Golubinka spring itself. Further, based on the discharge hydrograph, groundwater level oscillation was determined. The resulting spring discharge hydrograph and groundwater levels, along with the assumption of Golubinka spring as the only spring on the catchment, were used in creating turbulent seepage model of the fractured system within the aquifer, which evidently extends along the axis of the Golubinka spring catchment. The model yielded suitable turbulent seepage coefficients of the fracture system. By using the numerical model KarstMod it was estimated that, on average, concentrated fracture flow drains around 85% of infiltrated volumes and the remaining 15% accounts for diffuse matrix flow. Finally, the Modflow model was used in order to get insight into the flow field and the distribution of equipotentials in the aquifer of the Golubinka catchment.

Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer

2020 ◽  
Vol 20 (3) ◽  
pp. 909-921 ◽  
Author(s):  
Akbar Khedri ◽  
Nasrollah Kalantari ◽  
Meysam Vadiati
Keyword(s):  
Groundwater Level ◽  
Unconfined Aquifer ◽  
Least Square ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Data Set ◽  
Groundwater Levels ◽  
Statistical Performance Evaluation ◽  
Groundwater Level Prediction

Abstract Accurate and reliable groundwater level prediction is an important issue in groundwater resource management. The objective of this research is to compare groundwater level prediction of several data-driven models for different prediction periods. Five different data-driven methods are compared to evaluate their performances to predict groundwater levels with 1-, 2- and 3-month lead times. The four quantitative standard statistical performance evaluation measures showed that while all models could provide acceptable predictions of groundwater level, the least square support vector machine (LSSVM) model was the most accurate. We developed a set of input combinations based on different levels of groundwater, total precipitation, average temperature and total evapotranspiration at monthly intervals. For each model, the antecedent inputs that included Ht-1, Ht-2, Ht-3, Tt, ETt, Pt, Pt-1 produced the best-fit model for 1-month lead time. The coefficient of determination (R2) and the root mean square error (RMSE) were calculated as 0.99%, 1.05 meters for the train data set, and 95%, 2.3 meters for the test data set, respectively. It was also demonstrated that many combinations the above-mentioned approaches could model groundwater levels for 1 and 2 months ahead appropriately, but for 3 months ahead the performance of the models was not satisfactory.

scholarly journals Investigating Groundwater Condition and Seawater Intrusion Status in Coastal Aquifer Systems of Eastern India

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1952
Author(s):  
Subrata Halder ◽  
Lingaraj Dhal ◽  
Madan K. Jha
Keyword(s):  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Groundwater Level ◽  
Coastal Aquifer ◽  
Quality Data ◽  
Confined Aquifer ◽  
Groundwater Levels ◽  
Post Monsoon ◽  
Aquifer Systems ◽  
Sustainable Water Supply

Providing sustainable water supply for domestic needs and irrigated agriculture is one of the most significant challenges for the current century. This challenge is more daunting in coastal regions. Groundwater plays a pivotal role in addressing this challenge and hence, it is under growing stress in several parts of the world. To address this challenge, a proper understanding of groundwater characteristics in an area is essential. In this study, spatio-temporal analyses of pre-monsoon and post-monsoon groundwater-levels of two coastal aquifer systems (upper leaky confined and underlying confined) were carried out in Purba Medinipur District, West Bengal, India. Trend analysis of seasonal groundwater-levels of the two aquifers systems was also performed using Mann-Kendall test, Linear Regression test, and Innovative Trend test. Finally, the status of seawater intrusion in the two aquifers was evaluated using available groundwater-quality data of Chloride (Cl−) and Total Dissolve Solids (TDS). Considerable spatial and temporal variability was found in the seasonal groundwater-levels of the two aquifers. Further, decreasing trends were spotted in the pre-monsoon and post-monsoon groundwater-level time series of the leaky confined and confined aquifers, except pre-monsoon groundwater-levels in Contai-I and Deshpran blocks, and the post-monsoon groundwater-level in Ramnagar-I block for the leaky confined aquifer. The leaky confined aquifer in Contai-I, Contai-III, and Deshpran blocks and the confined aquifer in Nandigram-I and Nandigram-II blocks are vulnerable to seawater intrusion. There is an urgent need for the real-time monitoring of groundwater-levels and groundwater quality in both the aquifer systems, which can ensure efficient management of coastal groundwater reserves.

scholarly journals Evaluation of Seasonal Groundwater Quality Changes Associated with Groundwater Pumping and Level Fluctuations in an Agricultural Area, Korea

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 51
Author(s):  
Kyoochul Ha ◽  
Eunhee Lee ◽  
Hyowon An ◽  
Sunghyun Kim ◽  
Changhui Park ◽  
...  
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Groundwater Level ◽  
Irrigation Water ◽  
Agricultural Area ◽  
Water Type ◽  
Quality Changes ◽  
Groundwater Pumping ◽  
Rice Paddies ◽  
Groundwater Levels

This study was conducted to evaluate seasonal groundwater quality due to groundwater pumping and hydrochemical characteristics with groundwater level fluctuations in an agricultural area in Korea. Groundwater levels were observed for about one year using automatic monitoring sensors, and groundwater uses were estimated based on the monitoring data. Groundwater use in the area is closely related to irrigation for rice farming, and rising groundwater levels occur during the pumping, which may be caused by the irrigation water of rice paddies. Hydrochemical analysis results for two separate times (17 July and 1 October 2019) show that the dissolved components in groundwater decreased overall due to dilution, especially at wells in the alluvial aquifer and shallow depth. More than 50% of the samples were classified as CaHCO3 water type, and changes in water type occurred depending on the well location. Water quality changes were small at most wells, but changes at some wells were evident. In addition, the groundwater quality was confirmed to have the effect of saltwater supplied during the 2018 drought by comparison with seawater. According to principal component analysis (PCA), the water quality from July to October was confirmed to have changed due to dilution, and the effect was strong at shallow wells. In the study areas where rice paddy farming is active in summer, irrigation water may be one of the important factors changing the groundwater quality. These results provide a qualitative and quantitative basis for groundwater quality change in agricultural areas, particularly rice paddies areas, along with groundwater level and usage.

scholarly journals Effects of management decisions on genetic evaluation of simulated calving records using random regression

2021 ◽  
Author(s):  
M D MacNeil ◽  
J W Buchanan ◽  
M L Spangler ◽  
E Hay
Keyword(s):  
Reproductive Success ◽  
Simulated Data ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Management Decisions ◽  
Third Order ◽  
Data Set ◽  
Binary Phenotype ◽  
Random Regression Model ◽  
Missing Observation

Abstract The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2-11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given ten opportunities to calve. Culling open cows resulted in the EBV for 3 year-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 years of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that impact the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

scholarly journals Seismically induced changes in groundwater levels and temperatures following the ML5.8 (ML5.1) Gyeongju earthquake in South Korea

2021 ◽  
Author(s):  
Soo-Hyoung Lee ◽  
Jae Min Lee ◽  
Sang-Ho Moon ◽  
Kyoochul Ha ◽  
Yongcheol Kim ◽  
...  
Keyword(s):  
South Korea ◽  
Groundwater Level ◽  
Seismic Waves ◽  
Groundwater Resources ◽  
Fluid Pressure ◽  
Water Levels ◽  
Aquifer System ◽  
Groundwater Levels ◽  
Monitoring Wells ◽  
Gyeongju Earthquake

AbstractHydrogeological responses to earthquakes such as changes in groundwater level, temperature, and chemistry, have been observed for several decades. This study examines behavior associated with ML 5.8 and ML 5.1 earthquakes that occurred on 12 September 2016 near Gyeongju, a city located on the southeast coast of the Korean peninsula. The ML 5.8 event stands as the largest recorded earthquake in South Korea since the advent of modern recording systems. There was considerable damage associated with the earthquakes and many aftershocks. Records from monitoring wells located about 135 km west of the epicenter displayed various patterns of change in both water level and temperature. There were transient-type, step-like-type (up and down), and persistent-type (rise and fall) changes in water levels. The water temperature changes were of transient, shift-change, and tendency-change types. Transient changes in the groundwater level and temperature were particularly well developed in monitoring wells installed along a major boundary fault that bisected the study area. These changes were interpreted as representing an aquifer system deformed by seismic waves. The various patterns in groundwater level and temperature, therefore, suggested that seismic waves impacted the fractured units through the reactivation of fractures, joints, and microcracks, which resulted from a pulse in fluid pressure. This study points to the value of long-term monitoring efforts, which in this case were able to provide detailed information needed to manage the groundwater resources in areas potentially affected by further earthquakes.

scholarly journals TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling

2014 ◽  
Vol 14 (20) ◽  
pp. 10963-10976 ◽  
Author(s):  
J. J. P. Kuenen ◽  
A. J. H. Visschedijk ◽  
M. Jozwicka ◽  
H. A. C. Denier van der Gon
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Point Sources ◽  
Air Quality Modelling ◽  
Emission Data ◽  
Data Set ◽  
Spatially Distributed ◽  
Reported Data

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7 × 7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps.

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