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Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called 'noise'. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison. • Reporting model performance relative to data noise improves performance evaluation. • Multiscale performance evaluation supports judgments of data-driven models. • Simple bias correction improves our model performance at two of three scales. • Geostatistical scale metrics provide both noise quantification and fit evaluation. [ABSTRACT FROM AUTHOR]

In 2013 to 2015, 833 public supply wells in 15 Principal aquifers in the U.S. were sampled to identify which aquifers contained high methane concentrations (>1 mg/L) and determine the geologic, hydrologic, and geochemical conditions associated with high concentrations. This study represents the first national assessment of methane in aquifers used for public supply in the U.S. and, as such, advances the understanding of the occurrence and distribution of methane in groundwater nationally. Methane concentrations >1 and > 10 mg/L occurred in 6.7 and 1.1% of the samples, respectively. Most high concentrations occurred in aquifers in the Atlantic and Gulf Coastal Plain regions and upper Midwest. High methane concentrations were most commonly associated with Tertiary and younger aquifer sediments, old groundwater (>60 years), and concentrations of oxygen, nitrate-N, and sulfate <0.5 mg/L. Concentrations of methane were also positively correlated (p < 0.05) with dissolved organic carbon and ammonium. Case studies in Florida, Texas, and Iowa were used to explore how regional context from this data set could aid our understanding of local occurrences of methane in groundwater. Regional data for methane, Br/Cl ratios, sulfate, and other parameters helped identify mixing processes involving end members such as wastewater effluent-impacted groundwater, saline formation water, and pore water in glacial till that contributed methane to groundwater in some cases and supported methane oxidation in others. [ABSTRACT FROM AUTHOR]

Abstract: Groundwater chemistry and isotope data from 44 public supply wells in the Napa and Sonoma Valleys, California were determined to investigate mixing of relatively shallow groundwater with deeper hydrothermal fluids. Multivariate analyses including Cluster Analyses, Multidimensional Scaling (MDS), Principal Components Analyses (PCA), Analysis of Similarities (ANOSIM), and Similarity Percentage Analyses (SIMPER) were used to elucidate constituent distribution patterns, determine which constituents are significantly associated with these hydrothermal systems, and investigate hydrothermal contamination of local groundwater used for drinking water. Multivariate statistical analyses were essential to this study because traditional methods, such as mixing tests involving single species (e.g. Cl or SiO2) were incapable of quantifying component proportions due to mixing of multiple water types. Based on these analyses, water samples collected from the wells were broadly classified as fresh groundwater, saline waters, hydrothermal fluids, or mixed hydrothermal fluids/meteoric water wells. The Multivariate Mixing and Mass-balance (M3) model was applied in order to determine the proportion of hydrothermal fluids, saline water, and fresh groundwater in each sample. Major ions, isotopes, and physical parameters of the waters were used to characterize the hydrothermal fluids as Na–Cl type, with significant enrichment in the trace elements As, B, F and Li. Five of the wells from this study were classified as hydrothermal, 28 as fresh groundwater, two as saline water, and nine as mixed hydrothermal fluids/meteoric water wells. The M3 mixing-model results indicated that the nine mixed wells contained between 14% and 30% hydrothermal fluids. Further, the chemical analyses show that several of these mixed-water wells have concentrations of As, F and B that exceed drinking-water standards or notification levels due to contamination by hydrothermal fluids. [Copyright &y& Elsevier]

Abstract: Information on the ocean/atmosphere state over the period spanning the Last Glacial Maximum – from the Late Pleistocene to the Holocene – provides crucial constraints on the relationship between orbital forcing and global climate change. The Pacific Ocean is particularly important in this respect because of its dominant role in exporting heat and moisture from the tropics to higher latitudes. Through targeting groundwaters in the Mojave Desert, California, we show that noble gas derived temperatures in California averaged 4.2±1.1°C cooler in the Late Pleistocene (from ∼43 to ∼12ka) compared to the Holocene (from ∼10 to ∼5ka). Furthermore, the older groundwaters contain higher concentrations of excess air (entrained air bubbles) and have elevated oxygen-18/oxygen-16 ratios (δ18O) – indicators of vigorous aquifer recharge, and greater rainfall amounts and/or more intense precipitation events, respectively. Together, these paleoclimate indicators reveal that cooler and wetter conditions prevailed in the Mojave Desert from ∼43 to ∼12ka. We suggest that during the Late Pleistocene, the Pacific ocean/atmosphere state was similar to present-day El Nino-like patterns, and was characterized by prolonged periods of weak trade winds, weak upwelling along the eastern Pacific margin, and increased precipitation in the southwestern U.S. [Copyright &y& Elsevier]