Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México

Li, Yanmei; Hernández, Horacio; Avilés, Manuel; Knappett, Peter S.K.; Giardino, John R.; MIRANDA, RAÚL; Puy, María Jesús; Padilla, Francisco; Morales, Jorge L.

doi:10.1016/j.jhydrol.2019.124517

Cited by 32 publications

(28 citation statements)

References 37 publications

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“…Lift height, As and F concentrations, population IQ, and household income are all stocks or proxies for stocks in the model (Figure 1 ). The upstream side of the model (left side) quantifies how irrigation pumping benefits the farms and businesses that pump while simultaneously imposing a cost on themselves in the form of energy costs due to greater lift heights, which cuts into farm profit (Li et al., 2020 ).…”

Section: Methods and Datamentioning

confidence: 99%

“…Between 1980 and the present, the water tables have fallen at an average rate of 1.65 m/year (Figure S2 in Supporting Information S1 ) (Li et al., 2020 ). New wells must be drilled to reach the falling water table.…”

Section: Methods and Datamentioning

confidence: 99%

“…In 2020 the median depth to the water table was 100 m ( H o ) with 25th and 75th percentile depths of 70 and 150 m, respectively (Li et al., 2020 ). Combined pumping rates ( Q t ) across the basin for agriculture ( Q oag ) and domestic (residential) ( Q ores ) purposes in 2020 were obtained from CONAGUA (Table 3 ) (CONAGUA, 2020e ) and used to calculate the water deficit ( D t ) (Equation 4 in Table 2 ).…”

Section: Methods and Datamentioning

confidence: 99%

“…We leverage data we have assembled through our diverse teams working in this basin for 10 years. These data include basin‐wide demographic data, electricity subsidies, and water governance structures (Hoogesteger & Wester, 2017 ); irrigation needs for commonly grown crops (Torres Padilla, 2021 ); historical pumping rates and water table declines (Li et al., 2020 ); the distribution of As and F concentrations across time and space (Knappett et al., 2020 ); empirically measured dose‐response curves of IQ reduction associated with childhood exposure to varying As and F concentrations in drinking water (Rocha‐Amador et al., 2007 ); and the costs of the two most widely used drinking water mitigation options in Mexico (Del Razo et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Systems Approach to Remediating Human Exposure to Arsenic and Fluoride From Overexploited Aquifers

et al. 2022

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In semiarid agricultural regions, aquifers have watered widespread economic development. Falling water tables, however, drive up energy costs and can make the water toxic for human consumption. The study area is located in central Mexico, where arsenic and fluoride are widely present at toxic concentrations in well water. We simulated the holistic outcomes from three pumping scenarios over 100 years (2020–2120); (S1) pumping rates increase at a similar rate to the past 40 years, (S2) remain constant, or (S3) decrease. Under scenario S1, by 2120, the depth to water table increased to 426 m and energy consumption for irrigation increased to 4 × 10 9 kWh/yr. Arsenic and fluoride concentrations increased from 14 to 46 μg/L and 1.0 to 3.6 mg/L, respectively. The combined estimated IQ point decrements from drinking untreated well water lowered expected incomes in 2120 by 27% compared to what they would be with negligible exposure levels. We calculated the 100‐year Net Present Value (NPV) of each scenario assuming the 2020 average crop value to water footprint ratio of 0.12 USD/m 3 . Without drinking water mitigation, S1 and S3 yielded relative NPVs of −5.96 × 10 9 and 1.51 × 10 9 USD, respectively, compared to the base case (S2). The relative NPV of providing blanket reverse osmosis treatment, while keeping pumping constant (S2), was 11.55 × 10 9 USD and this gain increased when combined with decreased pumping (S3). If a high value, low water footprint crop was substituted (broccoli, 1.51 USD/m 3 ), the net gains from increasing pumping were similar in size to those of implementing blanket drinking water treatment.

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Section: Methods and Datamentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systems Approach to Remediating Human Exposure to Arsenic and Fluoride From Overexploited Aquifers

et al. 2022

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“…Wainwright et al [39] used a Bayesian model to integrate air dose rate datasets in the FDNPP area with the goal of improving the resolution and completeness. Bayesian kriging is used for capturing uncertainty in the spatial distribution of sparse [40] and non-Gaussian, non-stationary environmental data [41]. In this study, we analyze contrasting characteristics of crowdsourced and government data for the same phenomenon and demonstrate an approach to estimate the spatial distribution with uncertainty metrics.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Hultquist

Oravecz

Cervone

2021

IJGI

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Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making.

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Enhanced precipitation prediction using DEM‐based predictors and satellite imagery

Antal

Dumitrescu

Cheval

et al. 2023

Intl Journal of Climatology

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Over the last decades, climatic changes have triggered considerable impacts across the globe with detrimental effects on all ecosystems. Given the complexity of topography and climate, Romania is one of the most exposed countries in the South‐Eastern Europe to extreme hydrological events. As a consequence, the spatial distribution of precipitation is of greater importance for future analysis. This study examines the performance of Empirical Bayesian Kriging Regression Prediction (EBKRP) and Geographically Weighted Regression (GWR) to predict the spatial distribution of annual and seasonal precipitation over Romania. Twelve co‐variables derived from topography, and remote sensing products data were used to improve the prediction. The co‐variable selection process was performed prior to the regression model to reduce the complexity and processing time, using the Boruta Algorithm (BA), which is an innovative approach. The performance of BA was compared with Gini Coefficient. Our findings confirmed that 10 co‐variables were relevant to predict annual precipitation and nine for seasonal. The overall prediction of precipitation is more influenced by topography (altitude, slope, surface roughness) and the distance to marine bodies (Black Sea and Adriatic Sea). Cross‐validation and five statistical metrics were applied to assess the performance of the regression models. The results show similar spatial distribution pattern of precipitation, whereas the highest annual precipitation is found in the Carpathian Mountains (EBKRP: 1,399.2 mm, GWR: 1,249.7 mm) and the lowest in the lowlands (EBKRP: 355.9 mm, GWR: 346.8 mm). For seasons, both methods predicted the highest precipitation in summer and lowest in autumn. In all seasons, the precipitation is underestimated by both methods, however, for annual, only GWR does so. Our study revealed GWR as the best method to predict annual and seasonal precipitation over Romania, as it yielded the highest correlation coefficient Spearman (S), Pearson (P), determination coefficient (R2) and lowest mean absolute error (MAE) and root mean square error (RMSE).

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Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México

Cited by 32 publications

References 37 publications

A Systems Approach to Remediating Human Exposure to Arsenic and Fluoride From Overexploited Aquifers

A Systems Approach to Remediating Human Exposure to Arsenic and Fluoride From Overexploited Aquifers

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Enhanced precipitation prediction using DEM‐based predictors and satellite imagery

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