Discussion of “Depth and Spacing of Tile Drainage Systems”

1962 ◽  
Vol 88 (3) ◽  
pp. 59-62
Author(s):  
E. J. List
Keyword(s):  
Tile Drainage ◽  
Drainage Systems

scholarly journals Comparison of performance of tile drainage routines in SWAT 2009 and 2012 in an extensively tile-drained watershed in the Midwest

2017 ◽  
Author(s):  
Tian Guo ◽  
Margaret Gitau ◽  
Venkatesh Merwade ◽  
Jeffrey Arnold ◽  
Raghavan Srinivasan ◽  
...  
Keyword(s):  
Calculation Method ◽  
Assessment Tool ◽  
Curve Number ◽  
Surface Flow ◽  
Tile Drainage ◽  
Random Pattern ◽  
Field Site ◽  
Drainage Systems ◽  
Nitrate N ◽  
Tile Flow

Abstract. Subsurface tile drainage systems are widely used in agricultural watersheds in the Midwestern U.S. Tile drainage systems enable the Midwest area to become highly productive agricultural lands, but can also create environmental problems, for example nitrate-N contamination associated with drainage waters. The Soil and Water Assessment Tool (SWAT) has been used to model watersheds with tile drainage. SWAT2012 revisions 615 and 645 provide new tile drainage routines. However, few studies have used these revisions to study tile drainage impacts at both field and watershed scales. Moreover, SWAT2012 revision 645 improved the soil moisture based curve number calculation method, which has not been fully tested. This study used long-term (1991–2003) field site and river station data from the Little Vermilion River (LVR) watershed to evaluate performance of tile drainage routines in SWAT2009 revision 528 (the old routine) and SWAT2012 revisions 615 and 645 (the new routine). Both routines provided reasonable but unsatisfactory uncalibrated flow and nitrate loss results. Calibrated monthly tile flow, surface flow, nitrate-N in tile and surface flow, sediment and annual corn and soybean yield results from SWAT with the old and new tile drainage routines were compared with observed values. Generally, the new routine provided acceptable simulated tile flow (NSE = 0.50–0.68) and nitrate in tile flow (NSE = 0.50–0.77) for both field sites with random pattern tile and constant tile spacing, while the old routine simulated tile flow and nitrate in tile flow results for the field site with constant tile spacing were unacceptable (NSE = −0.77– −0.20 and −0.99–0.21 respectively). The new modified curve number calculation method in revision 645 (NSE = 0.56–0.82) better simulated surface runoff than revision 615 (NSE = −5.95 ~ 0.5). Calibration provided reasonable parameter sets for the old and new routines in LVR watershed, and the validation results showed that the new routine has the potential to accurately simulate hydrologic processes in mildly-sloped watersheds.

Discharge of tile drainage systems in an irrigated area of California

1956 ◽  
Vol 37 (4) ◽  
pp. 474
Author(s):  
A. F. Pillsbury ◽  
L. O Weeks ◽  
J. R. Spencer ◽  
R. C. Reeve
Keyword(s):  
Tile Drainage ◽  
Drainage Systems ◽  
Irrigated Area

Prediction of phosphorus concentration in tile-drainage water from the Montreal Lowlands soils

2003 ◽  
Vol 83 (1) ◽  
pp. 73-87 ◽  
Author(s):  
S. Beauchemin ◽  
R. R. Simard ◽  
M. A. Bolinder ◽  
M. C. Nolin ◽  
D. Cluis
Keyword(s):  
Management Practices ◽  
Drainage Water ◽  
Tile Drainage ◽  
Phosphorus Concentration ◽  
Surface Soils ◽  
Drainage Systems ◽  
Soil P ◽  
P Concentration ◽  
Soil Units ◽  
Total P

Subsurface drainage systems can be a significant pathway for P transfer from some soils to surface waters. The objective of the study was to determine P concentration in tile-drainage water and its relationship to P status in surface soils (A horizons) from an intensively cultivated area in the Montreal Lowlands. The profiles of 43 soil units were characterized for their P contents and pedogenic properties. Tile-drainage water P concentrations were monitored over a 3-y r period on a weekly basis on 10 soil units, and four times during each growing season for the other 33 units. The soil units were grouped into lower and higher P sorbing soils using multiple discriminant equations developed in an earlier related study. The A horizons of the lower P sorbing soils had an elevated P saturation degree [mean Mehlich(III) P/Al = 17%] associated with total P concentrations in tile-drainage water consistently greater than the surface water quality standard of 0.03 mg total P L-1. Conversely, low P concentrations in tile-drainage waters (< 0.03 mg L-1) and a moderate mean Mehlich(III) P/Al ratio of 8% were observed in the higher P sorbing soil group. Total P concentrations in drainage systems were significantly related to soil P status in surface soils. Grouping soils according to their P sorption capacities increased the power of prediction based on only one soil variable. However, accurate predictions in terms of drain P concentration can hardly be obtained unless large dataset and other factors related to field management practices and hydrology of the sites are also considered. Therefore, a better alternative to predict the risk of P leaching is to work in terms of risk classes and rely on a multiple factor index. Key words: Tile-drainage water, phosphorus, P transfer, P loss, degree of soil P saturation, phosphorus index

What pesticide legislation forgot about: Pesticide transport through hydraulic shortcuts

2020 ◽  
Author(s):  
Urs Schönenberger ◽  
Christian Stamm
Keyword(s):  
Surface Water ◽  
Surface Waters ◽  
Flow Direction ◽  
Arable Land ◽  
Tile Drainage ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Transport Pathway ◽  
Drainage Systems ◽  
Pesticide Transport

&lt;p&gt;Pesticides from agricultural origin may harm surface water quality and pose a risk for aquatic organisms. In Europe, the regulations on agricultural pesticide usage are currently focusing on &amp;#8220;classical&amp;#8221; pesticide transport pathways, such as surface runoff, spray drift into surface waters, or tile drainage flow. Recent studies have shown that in certain cases also so-called &lt;em&gt;hydraulic shortcuts&lt;/em&gt; (e.g. road storm drains, or manholes of the tile drainage systems) can be of major importance for pesticide transport into surface waters. However, until now research has widely neglected this transport pathway.&lt;/p&gt;&lt;p&gt;In this study, we investigated the relevance of hydraulic shortcuts for the pesticide transport from arable land to surface waters in Switzerland. We selected twenty small catchments throughout the Swiss midlands as study areas by performing a weighted random selection on a nation-wide hydrological catchment stratification dataset. On average, they have an area of 3.5&amp;#160;km&lt;sup&gt;2&lt;/sup&gt; with a fraction of 44 % of arable land. In the agricultural areas of these catchments, we mapped hydraulic shortcuts using different data sources: Field surveys, high-resolution aerial images captured by a fixed-wing drone as well as plans of the road storm drains and the tile drainage systems. Subsequently, we modelled the hydrological connectivity of arable areas to surface waters using a digital elevation model and a D-infinity flow direction algorithm. Within this model, we distinguished between areas with a direct and indirect (i.e. via shortcuts) surface water connectivity.&lt;/p&gt;&lt;p&gt;Our model results show that major fractions of the arable areas with surface water connectivity are not connected directly, but via hydraulic shortcuts: The fraction of indirectly connected areas ranges between 18&amp;#160;% and 90&amp;#160;%, with a median of 52&amp;#160;% for the 20 catchments. In order to check the model robustness we performed sensitivity analyses for different model parameters, such as sink filling depth, maximal flow length, or parameters addressing the influence of roads, forests, and hedges. In certain cases, changes of those model parameters have a strong influence on the absolute extent of directly and indirectly connected areas. However, their fractions compared to the total connected area were insensitive to changes in the model parameters.&lt;/p&gt;&lt;p&gt;In addition, we will present the results of a model predicting the fraction of arable land connected to shortcuts within a catchment, depending on auxiliary quantities (e.g. length of roads of a certain type, land use, slope). Using this model, we can estimate the arable land fraction per catchment on a national scale.&lt;/p&gt;

Design, construction and performance evaluation of small tile drainage systems in the Indus Basin

2007 ◽  
Vol 56 (S1) ◽  
pp. S217-S225 ◽  
Author(s):  
Muhammad Akram Kahlown ◽  
Muhammad Khan Marri ◽  
Muhammad Azam
Keyword(s):  
Performance Evaluation ◽  
Tile Drainage ◽  
Indus Basin ◽  
Drainage Systems ◽  
And Performance ◽  
Design Construction

Drainage equations for random and irregular tile drainage systems

2001 ◽  
Vol 48 (3) ◽  
pp. 207-224 ◽  
Author(s):  
R.A Cooke ◽  
S Badiger ◽  
A.M Garcı́a
Keyword(s):  
Tile Drainage ◽  
Drainage Systems

scholarly journals Predicting tile drainage discharge using machine learning algorithms

2020 ◽  
Author(s):  
Saghar Khodadad Motarjemi ◽  
Anders Bjørn Møller ◽  
Finn Plauborg ◽  
Bo Vangsø Iversen
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Models ◽  
Clay Content ◽  
Drainage Water ◽  
Tile Drainage ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Agricultural Fields ◽  
Drainage Systems

Abstract. Drainage systems can significantly improve the water management in agricultural fields. However, they may transport contaminants originating from fertilizers and pesticides and threaten ecosystems. Determining the quantity of drainage water is an important factor for constructed wetlands and other drainage mitigation techniques. This study was carried out in Denmark where tile drainage systems are implemented in more than half of the agricultural fields. The first aim of the study was to predict the annual discharge of tile drainage systems using machine-learning methods, which have been highly popular in recent years. The second objective was to assess the importance of the parameters and their impact on the predictions. Data from 53 drainage stations distributed in different regions of Denmark were collected and used for the analysis. The covariates contained 35 parameters including the calculated percolation and geographic variables such as drainage probability, clay content in different depth intervals, and elevation, all extracted from existing national maps. Random Forest and Cubist were selected as predictive models. Both models were trained on the dataset and used to predict yearly drainage discharge. Results highlighted the importance of the cross-validation methods and indicated that both Random Forest and Cubist can perform as predictive models with a low complexity and good correlation between predicted and observed discharge. Covariate importance analysis showed that among all of the used predictors, the percolation and elevation have the largest effect on the prediction of tile drainage discharge. This work opens up for a better understanding of the dynamics of tile drainage discharge and proves that machine-learning techniques can perform as predictive models in this specific concept. The developed models can be used in regard to a national mapping of expected tile drain discharge.

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