Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross‐validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003–2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.

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Summary Gastric acid secretion by parietal cells requires trafficking and exocytosis of H-K-ATPaserich tubulovesicles (TVs) toward apical membranes in response to histamine stimulation via cAMP elevation. Here, we found that TRPML1 (ML1), a protein that is mutated in type IV Mucolipidosis (ML-IV), is a tubulovesicular channel essential for TV exocytosis and acid secretion. Whereas ML-IV patients are reportedly achlorhydric, transgenic overexpression of ML1 in mouse parietal cells induced constitutive acid secretion. Gastric acid secretion was blocked and stimulated by ML1 inhibitors and agonists, respectively. Organelle-targeted Ca2+ imaging and direct patch-clamping of apical vacuolar membranes revealed that ML1 mediates a PKA-activated conductance on TV membranes that is required for histamine-induced Ca2+ release from TV stores. Hence, we demonstrated that ML1, acting as a Ca2+ channel in TVs, links transmitter-initiated cyclic nucleotide signaling with Ca2+-dependent TV exocytosis in parietal cells, providing a regulatory mechanism that could be targeted to manage acid-related gastric diseases.

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