scholarly journals Distinguishing Capillary Fringe Reflection in a GPR Profile for Precise Water Table Depth Estimation in a Boreal Podzolic Soil Field

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1670
Author(s):  
Chameera Illawathure ◽  
Mumtaz Cheema ◽  
Vanessa Kavanagh ◽  
Lakshman Galagedara
Keyword(s):  
Water Table ◽  
Shallow Groundwater ◽  
Depth Estimation ◽  
Water Table Depth ◽  
Soil Conditions ◽  
Capillary Fringe ◽  
Growing Seasons ◽  
Sampling Volume ◽  
Highly Correlated ◽  
Using Data

Relative permittivity and soil moisture are highly correlated; therefore, the top boundary of saturated soil gives strong reflections in ground-penetrating radar (GPR) profiles. Conventionally in shallow groundwater systems, the first dominant reflection comes from the capillary fringe, followed by the actual water table. The objective of this study was to calibrate and validate a site-specific relationship between GPR-estimated depth to the capillary fringe (DCF) and measured water table depth (WTDm). Common midpoint (CMP) GPR surveys were carried out in order to estimate the average radar velocity, and common offset (CO) surveys were carried out to map the water table variability in the 2017 and 2018 growing seasons. Also, GPR sampling volume geometry with radar velocities in different soil layers was considered to support the CMP estimations. The regression model (R2 = 0.9778) between DCF and WTDm, developed for the site in 2017, was validated using data from 2018. A regression analysis between DCF and WTDm for the two growing seasons suggested an average capillary height of 0.741 m (R2 = 0.911, n = 16), which is compatible with the existing literature under similar soil conditions. The described method should be further developed over several growing seasons to encompass wider water table variability.

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 High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model

2021 ◽  
Vol 3 ◽  
Author(s):  
Julian Koch ◽  
Jane Gotfredsen ◽  
Raphael Schneider ◽  
Lars Troldborg ◽  
Simon Stisen ◽  
...  
Keyword(s):  
High Resolution ◽  
Water Table ◽  
Spatial Resolution ◽  
Shallow Groundwater ◽  
Water Table Depth ◽  
Training Data ◽  
Gradient Boosting ◽  
Detailed Knowledge ◽  
Mitigation And Adaptation ◽  
Physically Based

Detailed knowledge of the uppermost water table representing the shallow groundwater system is critical in order to address societal challenges that relate to the mitigation and adaptation to climate change and enhancing climate resilience in general. Machine learning (ML) allows for high resolution modeling of the water table depth beyond the capabilities of conventional numerical physically-based hydrological models with respect to spatial resolution and overall accuracy. For this, in-situ well and proxy observations are used as training data in combination with high resolution covariates. The objective of this study is to model the depth of the uppermost water table for a typical summer and winter condition at 10 m spatial resolution over entire Denmark (43,000 km2). CatBoost, a state of the art implementation of gradient boosting decision trees, is employed in this study to model the water table depth and the associated uncertainties. The groundwater domain has not been the most prominent field of applications of recent hydrological ML advances due to the lack of big data. This study brings forward a novel knowledge-guided ML framework to overcome this limitation by integrating simulation results from a physically-based groundwater flow model. The simulation data are utilized to (1) identify wells that represent the uppermost water table, (2) augment missing training data by accounting for simulated water level seasonality, and (3) expand the list of covariates. The curated training dataset contains around 13,000 wells, 19,000 groundwater proxy observations at lakes, streams and coastline as well as 15 covariates. Cross validation attests that the ML model generalizes well with a mean absolute error of around 115 cm considering solely well observations and a MAE of <50 cm taking also the proxy observations into consideration. Quantile regression is applied to estimate confidence intervals and the estimated uncertainty is largest for moraine clay soils that are characterized with a distinct geological heterogeneity. This study highlights a novel research avenue of knowledge-guided ML for the groundwater domain by efficiently supporting a ML model with a physically-based hydrological model to predict the depth of the water table at unprecedented spatial detail and accuracy.

Land use as possible strategy for managing water table depth in flat basins with shallow groundwater

2017 ◽  
Vol 16 (1) ◽  
pp. 79-92 ◽  
Author(s):  
Pablo E. García ◽  
Angel N. Menénendez ◽  
Guillermo Podestá ◽  
Federico Bert ◽  
Poonam Arora ◽  
...  
Keyword(s):  
Land Use ◽  
Water Table ◽  
Shallow Groundwater ◽  
Water Table Depth

Monitoring of natural regeneration in a mixed deciduous forest

1999 ◽  
Vol 64 ◽  
Author(s):  
K. M. Tabari ◽  
N. Lust
Keyword(s):  
Survival Rate ◽  
Natural Regeneration ◽  
Deciduous Forest ◽  
Seedling Stage ◽  
Permanent Plots ◽  
Soil Conditions ◽  
Growing Seasons ◽  
Humus Layer ◽  
Beech Stand ◽  
Regeneration Phase

Monitoring  of natural regeneration in a dense semi-natural mixed hardwood forest on the  base    of ash, beech, oak and sycamore occurred over 3 years in the Aelmoeseneie  experimental    forest, Belgium. 40 permanent plots (4 m x 5 m) were selected in three  various humus types,    located in an ash stand and in an oak - beech stand. In all plots abundance  and top height of all    broad leaved regenerated species were determined at the end of the growing  seasons 1995 and    1998. In addition, the seedlings which appeared in the plots during 1996  and 1997 were    identified and followed up.    This study proves that in the investigated sites natural regeneration is  drastically poor and    diversity is low, in particular where the humus layer is more acidic (mull  moder) and the litter    layer is thick. No regeneration phase older than the seedling stage (h <  40 cm) is developed on    the different humus types. On average, total number of seedlings in 1995  amounts to 38    units/are in the ash stand and to 63 units/are in the oak - beech stand.  Survival rate over a 3-    year period is 37% and 42% respectively in the ash and oak - beech stands.  Total ingrowth    during the growing seasons 1996 and 1997 is virtually poor, indicating 16  and 8 units/are    respectively in above mentioned stands. Survival rate of occurring  seedlings, as well as the ingrowth of new seedlings are notably different (P < 0.05) according to the soil conditions of the    ash stand. Generally, the low presence of seedlings and the lack of  regeneration older than the    seedling stage reveal that the regeneration development encounters with a  critical problem. The    continuation of this process would likely result in a progressive  succession by the invasive and    the unwanted tree species.

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.

scholarly journals Genotype-Dependent Tipburn Severity During Lettuce Hydroponic Culture Is Associated with Altered Nutrient Leaf Content

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 616
Author(s):  
Virginia Birlanga ◽  
José Ramón Acosta-Motos ◽  
José Manuel Pérez-Pérez
Keyword(s):  
Low Cost ◽  
Nutrient Content ◽  
Leafy Vegetables ◽  
Production Methods ◽  
Growing Seasons ◽  
Lactuca Sativa L ◽  
Hydroponic Cultivation ◽  
Highly Correlated ◽  
Nutrient Solutions ◽  
Nutrient Traits

Cultivated lettuce (Lactuca sativa L.) is one of the most important leafy vegetables in the world, and most of the production is concentrated in the Mediterranean Basin. Hydroponics has been successfully utilized for lettuce cultivation, which could contribute to the diversification of production methods and the reduction of water consumption and excessive fertilization. We devised a low-cost procedure for closed hydroponic cultivation and easy phenotyping of root and shoot attributes of lettuce. We studied 12 lettuce genotypes of the crisphead and oak-leaf subtypes, which differed on their tipburn resistance, for three growing seasons (Fall, Winter, and Spring). We found interesting genotype × environment (G × E) interactions for some of the studied traits during early growth. By analyzing tipburn incidence and leaf nutrient content, we were able to identify a number of nutrient traits that were highly correlated with cultivar- and genotype-dependent tipburn. Our experimental setup will allow evaluating different lettuce genotypes in defined nutrient solutions to select for tipburn-tolerant and highly productive genotypes that are suitable for hydroponics.

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