Development and credibility assessment of a metamodel relating water table depth to agricultural production

Heuvelmans, Griet

doi:10.1016/j.agwat.2010.06.004

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Each soil texture has a different but slightly overlapping range of optimum WTD, which becomes deeper as the soil texture is finer. In most years, the optimum WTD in sandy loam ranges from 0.5 to 0.7 m, while loam ranges from 0.7 to 0.9 and silt loam is from 0.8 to 1.0 m. Our optimum WTD results for sandy loam largely agree with the results of Heuvelmans [], who determined an optimum WTD for corn on sand of 0.25–1.0 m in Belgium. For a given growing season, the magnitude of the groundwater yield subsidy at the optimum WTD also tends to be smallest in the fine‐grained silt loam soils, supporting our field results indicating that the potential benefits of shallow groundwater are strongest in soils with the weakest ability to retain water.…”

Section: Resultssupporting

confidence: 87%

“…Results from our factorial experiment indicate that both the optimum WTD and the groundwater yield subsidy are controlled by a combination of soil texture and growing season weather. While previous studies have demonstrated that the extinction depth is deeper when soil texture is finer or rooting depth is deeper [ Shah et al ., ; Heuvelmans , ], variability in optimum WTD in response to growing season weather conditions is poorly known. Florio et al .…”

Section: Resultsmentioning

confidence: 99%

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Untangling the effects of shallow groundwater and soil texture as drivers of subfield‐scale yield variability

Zipper

Soylu

Booth

et al. 2015

Water Resources Research

114

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Water table depth (WTD), soil texture, and growing season weather conditions all play critical roles in determining agricultural yield; however, the interactions among these three variables have never been explored in a systematic way. Using a combination of field observations and biophysical modeling, we answer two questions: (1) under what conditions can a shallow water table provide a groundwater yield subsidy and/or penalty to corn production?; and (2) how do soil texture and growing season weather conditions influence the relationship between WTD and corn yield?. Subfield-scale yield patterns during a dry (2012) and wet (2013) growing season are used to identify sensitivity to weather. Areas of the field that are negatively impacted by wet growing seasons have the shallowest observed WTD (<1 m), while areas with consistently strong yield have intermediate WTD (1-3 m). Parts of the field that perform consistently poorly are characterized by deep WTD (>3 m) and coarse soil textures. Modeling results find that beneficial impacts of shallow groundwater are more common than negative impacts under the conditions studied, and that the optimum WTD is shallower in coarser soils. While groundwater yield subsidies have a higher frequency and magnitude in coarse-grained soils, the optimum WTD responds to growing season weather at a relatively constant rate across soil types. We conclude that soil texture defines a baseline upon which WTD and weather interact to determine overall yield. Our work has implications for water resource management, climate/land use change impacts on agricultural production, and precision agriculture.

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Section: Resultssupporting

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Untangling the effects of shallow groundwater and soil texture as drivers of subfield‐scale yield variability

Zipper

Soylu

Booth

et al. 2015

Water Resources Research

114

View full text Add to dashboard Cite

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“…Groundwater applications of metamodels include multiobjective management of groundwater lenses on small islands (Ataie‐Ashtiani et al, ), simulating agriculturally induced pumping stresses on a shallow water table (Heuvelmans, ), simulating groundwater response to sea level rise on a barrier island (Fienen et al, ), and, in the LMB, evaluating the surface water contribution to pumped wells (Fienen et al, ). In the latter study, statistical learning approaches comprising Bayesian networks (BNs), artificial neural networks (ANNs), and gradient‐boosted regression trees (GBRTs) were compared for their ability to extend MODFLOW predictions to unsampled areas.…”

Section: Introductionmentioning

confidence: 99%

Metamodeling for Groundwater Age Forecasting in the Lake Michigan Basin

Fienen

Nolan

Kauffman

et al. 2018

Water Resources Research

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Groundwater age is an important indicator of groundwater susceptibility to anthropogenic contamination and a key input to statistical models for forecasting water quality. Numerical models can provide estimates of groundwater age, enabling interpretation of measured age tracers. However, to extend to national‐scale groundwater systems where numerical models are not routinely available, a more efficient metamodeling approach can provide a less precise but widely applicable estimate of groundwater age, trained to make forecasts based on predictor variables that can be measured independent of numerical models. We trained gradient‐boosted regression tree statistical metamodels to MODFLOW/MODPATH‐derived groundwater age estimates in five inset models in the Lake Michigan Basin, USA. Using high‐throughput computing, we explored an exhaustive range of tuning parameters and tested metamodels through cross validation, a 20% holdout, and a round robin approach among the five inset models withholding each inset model from training and testing on the held‐out inset model. Forecast skill—measured by Nash Sutcliffe efficiency—was high for age‐related responses in the 20% hold‐out case (ranging from 0.73 to 0.84). The round robin analysis provided the opportunity to explore extending to unmodeled areas and a greater range of skill indicated the need to evaluate when it is appropriate to apply a metamodel from one region to another. We further explored the ramifications of metamodel simplification achieved through removing predictor variables based on their estimated importance. We found that similar metamodel performance was achievable with a fraction of the candidate set of predictor variables with well construction variables being most important.

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“…In the following decades, field observations also showed the dependence of evaporation-induced water table recession on initial water table depth (Hellwig, 1973;Xiao et al, 2011;Zhang et al, 2018). Therefore, the concept of extinction depth has been widely employed to characterize whether groundwater evaporation is negligible (Baird and Maddock, 2005;Heuvelmans, 2010;Liu et al, 2015;Mughal et al, 2015;Balugani et al, 2017;Ma et al, 2019). Namely, when the water table depth is deeper than the extinction depth, there is little groundwater evaporation; when the water table depth is shallower than the extinction depth, the groundwater evaporation rate increases with decreasing water table depth.…”

Section: Introductionmentioning

confidence: 99%