Mapping Water Table Depth Using Geophysical and Environmental Variables

Buchanan, Sam; Triantafilis, John

doi:10.1111/j.1745-6584.2008.00490.x

Cited by 104 publications

(51 citation statements)

References 52 publications

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“…The RMSE of traditional approaches, as reported in the literature, range between 0.75 and 2.25 m [46,47], which is considerably greater than the errors that we have reported. However, it is clear that our method cannot completely replace ground-based techniques.…”

Section: Discussioncontrasting

confidence: 46%

A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles

et al. 2017

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Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they are generally difficult to scale. In this study, we present a novel workflow for mapping groundwater using orthophotography and photogrammetric point clouds acquired from unmanned aerial vehicles. Our approach takes advantage of the fact that pockets of surface water are normally abundant in peatlands, which we assume to be reflective of GWL in these porous, gently sloping environments. By first classifying surface water and then extracting a sample of water elevations, we can generate continuous models of GWL through interpolation. Estimates of DTW can then be obtained through additional efforts to characterize terrain. We demonstrate our methodology across a complex, 61-ha treed bog in northern Alberta, Canada. An independent accuracy assessment using 31 temporally coincident water-well measurements revealed accuracies (root mean square error) in the 20-cm range, though errors were concentrated in small upland pockets in the study area, and areas of dense tree covers. Model estimates in the open peatland areas were considerably better.

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Section: Discussioncontrasting

confidence: 46%

A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles

et al. 2017

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“…For mapping of water table depth, the approach of interpolation has either been deterministic, such as inverse distance weighting (IDW) (Gambolati and Volpi 1979;Buchanan and Triantafilis 2009;Sun et al 2009;Varouchakis and Hristopulos 2013;Arslan 2014) and radial basis function (RBF) (Sun et al 2009;Arslan 2014) or stochastic, such as ordinary kriging (OK) (Desbarats et al 2002;Kumar and Ramadevi 2006;Ahmadi and Sedghamiz 2008;Sun et al 2009;Varouchakis and Hristopulos 2013;Arslan 2014) and universal kriging (UK) (Reed et al 2000;Kumar and Ahmed 2003;Kumar 2007;Sun et al 2009, Varouchakis andHristopulos 2013).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, geostatistical interpolation techniques (kriging) utilize the statistical properties of the sample points. It quantifies the spatial autocorrelation among sampling points and accounts for the spatial configuration of the sampling points around the prediction location (Buchanan and Triantafilis 2009).…”

Section: Introductionmentioning

confidence: 99%

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

Adhikary

Dash

2014

Appl Water Sci

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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 postmonsoon 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 R 2 over IDW. The isodepth-area curves indicated the possibility of exploitation of groundwater up to a depth of 20 m.

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“…This scheme has been applied to agricultural areas by Finke et al (2004) and to nature conservation areas by Hoogland et al (2010). Spatial interpolation approaches can include ancillary data such as mapped geophysical parameters (Buchanan and Triantafilis, 2009). Statistical approaches strongly rely on both the quantity and quality of the data on the target variable itself, i.e., the water level data.…”

Section: Bechtold Et Al: Large-scale Regionalization Of Water Tabmentioning

confidence: 99%

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

Bechtold¹,

Tiemeyer²,

Laggner³

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Fluxes of the three main greenhouse gases (GHG) CO 2 , CH 4 and N 2 O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WL t ). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WL t is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45 % of WL t variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WL t and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WL t variance cannot be explained by nationally available predictor variables and that predictors with stronger WL t indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

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Mapping Water Table Depth Using Geophysical and Environmental Variables

Cited by 104 publications

References 52 publications

A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles

A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles

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

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

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