Groundwater Level Changes Due to Extreme Weather—An Evaluation Tool for Sustainable Water Management

Ziolkowska, Jadwiga R.; Reyes, Reuben

doi:10.3390/w9020117

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Also, surface water resources can be replenished by adequate precipitation, groundwater sources might be nonreplenishable once exploited or may take several years. While these conditions make groundwater resources extremely valuable, groundwater monitoring and measurements have been inconsistent geographically and over time (Ziolkowska & Reyes, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…However, groundwater-level data are often irregularly sampled, leading to temporal gaps in the record, and are not adequately distributed spatially across an aquifer (Marchant & Bloomfield, 2018;Oikonomou et al, 2018;Varouchakis & Hristopulos, 2013). The spatial sparseness of data presents challenges when spatially interpolating potentiometric surfaces and creating groundwater maps (Marchant & Bloomfield, 2018;Ruybal et al, 2019;Ziolkowska & Reyes, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Minimizing Errors in the Prediction of Water Levels Using Kriging Technique in Residuals of the Groundwater Model (MODFLOW)

Asadi¹,

Adhikari²

2021

Preprint

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Groundwater monitoring and water level predictions have been a challenging issue due to the complexity of groundwater movement. Simplified numerical simulation models have been used to represent the groundwater system; these models however only provide the conservative approximation of the system and may not always capture the local variations. Several other efforts such as coupling groundwater models with hydrological models and using geostatistical methods are being practiced to accurately predict the groundwater levels. In this study, we present a novel application of geostatistical tool on residuals of groundwater model. Kriging method was applied on the residuals of the numerical model (MODFLOW) generated by the TWDB (Texas Water Development Board) for the Edwards-Trinity (Plateau) aquifer. The study was done for the years 1995 through 2000 where 90% of the observation data was used for model simulation followed by cross-validation with the remaining 10% of the observations. The kriging method reduced the average absolute error of approximately 31 m (for MODFLOW simulation) to less than 5 m. Also, the residuals’ average standard error reduced from 9.7 to 4.7. This implies that the mean value of residuals over entire period can be a good estimation for each year separately. The use of kriging technique thus can provide with improved monitoring of groundwater levels resulting in more accurate potentiometric surface maps.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimizing Errors in the Prediction of Water Levels Using Kriging Technique in Residuals of the Groundwater Model (MODFLOW)

Asadi¹,

Adhikari²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, Rinehart et al () present groundwater maps for different groundwater basins in New Mexico and the spatial sparseness of wells limits the extent of the generated maps. Ziolkowska and Reyes () use a spatial and temporal model to evaluate groundwater‐level changes in Oklahoma and Texas from 2003 to 2014. Sparse groundwater‐level data prior to 2002 limits the temporal period of the study due to the potential to compromise the results.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging

Ruybal

Hogue

McCray

2019

Water Resources Research

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Groundwater monitoring is fundamental to understanding system dynamics, trends in storage, and the long‐term sustainability of an aquifer. Water‐level data are the key source of information used to understand the response. However, groundwater‐level data are often irregularly sampled, leading to temporal gaps in the record, and are not adequately distributed spatially across an aquifer. This presents challenges when spatially interpolating potentiometric surfaces and creating groundwater maps due to data availability. We present a spatiotemporal kriging methodology to improve spatial and temporal confidence in groundwater‐level predictions at unsampled locations. The space–time data set consists of a trend and residual component modeled with a linear regression and utilize a sum‐metric model to represent spatiotemporal covariances. The Arapahoe aquifer is used as a case study to demonstrate the benefits of spatiotemporal kriging over spatial kriging across a sparsely gauged and irregularly sampled aquifer. The Arapahoe aquifer is a major source of water for many residents along the Rocky Mountain Front Range in Colorado. The results show superior performance of spatiotemporal kriging to predict groundwater levels over the traditional spatial kriging. Spatiotemporal kriging represents realistic temporal and spatial changes in water levels and avoids some of the problems inherent to spatial kriging. This study demonstrates the power of spatiotemporal kriging to help inform system dynamics in irregularly sampled aquifers.

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Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

Nguyen

Costache

et al. 2021

Water Resour Manage

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Groundwater Level Changes Due to Extreme Weather—An Evaluation Tool for Sustainable Water Management

Cited by 10 publications

References 33 publications

Minimizing Errors in the Prediction of Water Levels Using Kriging Technique in Residuals of the Groundwater Model (MODFLOW)

Minimizing Errors in the Prediction of Water Levels Using Kriging Technique in Residuals of the Groundwater Model (MODFLOW)

Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging

Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

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