Water table depth data for use in modelling residential building ground-coupled heat transfer

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.

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

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210066 ◽

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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The distinct roles of water table depth and soil properties in controlling alternative woodland-grassland states in the Cerrado

Oecologia ◽

10.1007/s00442-021-04869-z ◽

2021 ◽

Author(s):

Jonathan W. F. Ribeiro ◽

Natashi A. L. Pilon ◽

Davi R. Rossatto ◽

Giselda Durigan ◽

Rosana M. Kolb

Keyword(s):

Soil Properties ◽

Water Table ◽

Water Table Depth

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Transient Analysis of Heat Transfer Across the Residential Building Roof with Pcm and Wood Wool- A Case Study by Numerical Simulation Approach

Archives of Civil Engineering ◽

10.2478/ace-2013-0026 ◽

2013 ◽

Vol 59 (4) ◽

pp. 483-497 ◽

Cited By ~ 3

Author(s):

D. Prakash ◽

P. Ravikumar

Keyword(s):

Heat Transfer ◽

Numerical Simulation ◽

Phase Change ◽

Phase Change Material ◽

Transient Analysis ◽

Residential Building ◽

Building Roof ◽

Type 3 ◽

Change Material

Abstract In this paper, transient analysis on heat transfer across the residential building roof having various materials like wood wool, phase change material and weathering tile is performed by numerical simulation technique. 2-dimensional roof model is created, checked for grid independency and validated with the experimental results. Three different roof structures are included in this study namely roof with (i). Concrete and weathering tile, (ii). Concrete, phase change material and weathering tile and (iii). Concrete, phase change material, wood wool and weathering tile. Roof type 3 restricts 13% of heat entering the room in comparison with roof having only concrete and weathering tile. Also the effect of various roof layers’ thickness in the roof type 3 is investigated and identified that the wood wool plays the major role in arresting the entry of heat in to the room. The average reduction of heat is about 10 % for an increase of a unit thickness of wood wool layer.

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Role of tree stand evapotranspiration in maintaining satisfactory drainage conditions in drained peatlands

Canadian Journal of Forest Research ◽

10.1139/x10-084 ◽

2010 ◽

Vol 40 (8) ◽

pp. 1485-1496 ◽

Cited By ~ 48

Author(s):

Sakari Sarkkola ◽

Hannu Hökkä ◽

Harri Koivusalo ◽

Mika Nieminen ◽

Erkki Ahti ◽

...

Keyword(s):

Bulk Density ◽

Water Table ◽

Interactive Effect ◽

Late Summer ◽

Water Table Depth ◽

Separate Analysis ◽

Tree Stand ◽

Stand Volume ◽

Linear Effect ◽

Subsequent Decrease

Ditch networks in drained peatland forests are maintained regularly to prevent water table rise and subsequent decrease in tree growth. The growing tree stand itself affects the level of water table through evapotranspiration, the magnitude of which is closely related to the living stand volume. In this study, regression analysis was applied to quantify the relationship between the late summer water table depth (DWT) and tree stand volume, mean monthly summertime precipitation (Ps), drainage network condition, and latitude. The analysis was based on several large data sets from southern to northern Finland, including concurrent measurements of stand volume and summer water table depth. The identified model demonstrated a nonlinear effect of stand volume on DWT, a linear effect of Ps on DWT, and an interactive effect of both stand volume and Ps. Latitude and ditch depth showed only marginal influence on DWT. A separate analysis indicated that an increase of 10 m3·ha–1 in stand volume corresponded with a drop of 1 cm in water table level during the growing season. In a subsample of the data, high bulk density peat showed deeper DWT than peat with low bulk density at the same stand volume.

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