Search

Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios. Key Points: Consistent, reproducible estimates of monthly water withdrawals were generated at the spatial resolution of USA water service areasSpatial and temporal variability of public supply withdrawal is driven by a complex array of explanatory featuresAn operational machine learning framework was developed to estimate water withdrawals, prediction uncertainty, and feature importance [ABSTRACT FROM AUTHOR]

Geomechanical models are often used to predict the impact on land surface of fluid withdrawal from deep reservoirs, as well as investigating measures for mitigation. The ability to accurately simulate surface displacements, however, is often impaired by limited information on the geomechanical parameters characterizing the geological formations of interest. In this study, we employ an ensemble smoother, a data assimilation algorithm, to provide improved estimates of reservoir parameters through assimilation of measurements of both horizontal and vertical surface displacement into geomechanical model results. The method leverages the demonstrated potential of remote sensing techniques developed in the last decade to provide accurate displacement data for large areas of the land surface. For evaluation purposes, the methodology is applied to the case of a disk-shaped reservoir embedded in a homogeneous, isotropic, and linearly elastic half space, subject to a uniform change in fluid pressure. Multiple sources of uncertainty are investigated, including the radius, R, the thickness, h, and the depth, c, of the reservoir; the pore pressure change, Δp; porous medium's vertical uniaxial compressibility, cM, and Poisson's ratio, ν, and the ratio, s, between the compressibilities of the medium during loading and unloading cycles. Results from all simulations show that the ensemble smoother has the capability to effectively reduce the uncertainty associated with those parameters to which the variability and the spatial distribution of land surface displacements are most sensitive, namely, R, c, cM, and s. These analyses demonstrate that the estimation of these parameters values depends on the number of measurements assimilated and the error assigned to the measurement values. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]